Image analysis for breast cancer prognosis

ABSTRACT

Heterogeneity for biomarkers in a tissue sample can be calculated. A heterogeneity score can be combined with an immunohistochemistry combination score to provide breast cancer recurrence prognosis. Heterogeneity can be based on percent positivity determinations for a plurality of biomarkers according to how many cells in the sample stain positive. An immunohistochemistry combination score can be calculated. An imaging tool can support a digital pathologist workflow that includes designating fields of view in an image of the tissue sample. Based on the fields of view, a heterogeneity metric can be calculated and combined with an immunohistochemistry combination score to generate a breast cancer recurrence prognosis score.

This application is a U.S. National Phase application ofPCT/EP2013/077295, filed Dec. 19, 2013, entitled “IMAGE ANALYSIS FORBREAST CANCER PROGNOSIS”, which claims the benefit under 35 U.S.C. §119(e) of and priority to U.S. Provisional Patent Application No.61/747148, filed Dec. 28, 2012, which is herein incorporated byreference in its entirety.

FIELD

This application relates to image analysis for prognosing breast cancer,such as early stage breast cancer.

PARTIES TO JOINT RESEARCH AGREEMENT

Ventana Medical Systems, Inc., Cleveland Clinic, and the University ofMelbourne are parties to joint research agreements governing inventionsdisclosed herein.

BACKGROUND

Patients with localized (early stage, resectable) breast cancerundergoing curative surgery have an underlying risk of local or distantcancer recurrence, and those people who will recur show an increasedmortality rate. Depending on the size of risk, different treatmentoptions exist. Thus, an assay that can reliably identify patients with alow or high risk of cancer recurrence is needed. Accordingly,technologies are also needed that can reliably discriminate between highand low risk patients and provide healthcare providers with additionalinformation to consider when determining a patient's treatment options.

SUMMARY

The present application provides computer-implemented methods for breastcancer prognosis. For example, the method can include generating abreast cancer recurrence prognosis score based at least on measuredprotein heterogeneity for a biomarker among a plurality of digitalfields of view within a displayed image depicting a breast cancer sampledetectably labeled with antibodies for the biomarker and animmunohistochemistry combination score for a subject; and outputting anindication of breast cancer recurrence prognosis for the subject basedon the breast cancer recurrence prognosis score. Based on these methods,also provided are one or more non-transitory computer-readable mediathat include computer-executable instructions causing a computing systemto perform the disclosed methods.

Also provided are computer-implemented methods. In one example, suchmethods include a slide image processing tool operable to receive aplurality of slide images depicting protein expression for respectivebiomarkers in a breast cancer sample from a subject; wherein the slideimage processing tool is operable to further receive fields of viewwithin the slide images; wherein the slide image processing tool isoperable to calculate an immunohistochemistry combination score based onthe slide images and fields of view within the slide images; wherein theslide image processing tool is operable to calculate one or moreheterogeneity scores based on the slide images and selections of fieldsof view within the slide images; and a prognosis tool operable to acceptthe immunohistochemistry combination score and the one or moreheterogeneity scores as input and output an indication of whether canceris likely to recur in the subject.

The disclosure also provides computer-implemented methods which caninclude displaying an indication of breast cancer recurrence prognosis.Such methods can include combining an immunohistochemistry combinationscore and a heterogeneity score into a breast cancer recurrenceprognosis score; and displaying an indication of breast cancerrecurrence prognosis based on the breast cancer recurrence prognosisscore.

Computer-implemented methods are provided that include receiving aplurality of digital fields of view within a displayed image depicting abreast cancer sample detectably labeled with antibodies for a biomarker;measuring protein expression for the biomarker in the digital fields ofview; measuring heterogeneity of measured protein expression for thebiomarker among the plurality of digital fields of view; and outputtingmeasured protein heterogeneity for the biomarker.

Computer-implemented methods are provided that include calculating animmunohistochemistry combination score for a subject, the methodcomprising: for a plurality of biomarkers, receiving respectivepluralities of digital fields of view within respective images depictinga breast cancer sample detectably labeled with respective biomarkerantibodies; measuring percent positivity for a plurality of thebiomarkers; calculating the immunohistochemistry combination score,wherein calculating the immunohistochemistry combination score comprisescombining the percent positivity for one biomarker with the percentpositivity for a second biomarker; and outputting theimmunohistochemistry combination score.

Computer-implemented methods are provided that include for ER, receivinga plurality of digital fields of view in an image depicting a breastcancer sample detectably labeled with an antibody for ER; for PR,receiving a plurality of digital fields of view in an image depicting abreast cancer sample detectably labeled with an antibody for ER; forKi-67, receiving a plurality of digital fields of view in an imagedepicting a breast cancer sample detectably labeled with an antibody forER; for HER2, receiving a plurality of digital fields of view in animage depicting a breast cancer sample detectably labeled with anantibody for ER; based on the digital fields of view for ER, calculatingan H-score for ER; based on the digital fields of view for PR,calculating a percent positivity for PR; based on the digital fields ofview for Ki-67, calculating a percent positivity for Ki-67; based on thedigital fields of view for HER2, calculating a binned score for HER2;and combining the H-score for ER, the percent positivity for PR, thepercent positivity for Ki-67, and the binned score for HER2 into animmunohistochemistry combination score.

Methods of prognosing or prognosticating breast cancer in a subject areprovided. In some examples, such a method includes selecting in a breastcancer sample obtained from the subject at least two different fields ofview (FOVs) for each of estrogen receptor (ER), human epidermal growthfactor receptor 2 (HER2), Ki-67 and progesterone receptor (PR), whereinthe sample is detectably labeled with antibodies for each of ER, HER2,Ki-67 and PR; measuring ER, HER2, Ki-67 and PR protein expression ineach of the selected FOV; determining an immunohistochemistry (IHC)combination score; measuring ER and PR protein heterogeneity in each ofthe selected FOVs; determining a protein heterogeneity score for each ofER and PR; combining the protein heterogeneity score and the IHCcombination score, thereby generating an output prognosis score; anddetermining that the breast cancer in the subject is likely to beaggressive if the output prognosis score meets a threshold value ordetermining that the breast cancer in the subject is unlikely to beaggressive if the output prognosis score does not meet the thresholdvalue.

Digital fields of view in images of a breast cancer sample from asubject detectably labeled with antibodies for a biomarker can bereceived and processed to measure protein heterogeneity for thebiomarker.

Heterogeneity measurements can be combined with an immunohistochemistrycombination score to generate a breast cancer recurrence prognosisscore.

Such a score can provide more information than the immunohistochemistrycombination score standing alone.

A digital pathologist workflow can be supported to facilitate field ofview selection on images.

The foregoing and other objects and features of the disclosure willbecome more apparent from the following detailed description, whichproceeds with reference to the accompanying figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an exemplary system for carrying out thetechnologies described herein.

FIG. 2 is a block diagram showing how images of slides stained fordifferent proteins are used to generate a breast cancer recurrenceprognosis score.

FIG. 3 is a schematic showing an overview of the claimed method.

FIG. 4 A-B are images showing exemplary annotations on fields of view.

FIG. 5 is a schematic showing an overview of the claimed method.

FIG. 6 is a schematic drawing showing exemplary steps of obtainingimmunohistochemistry (IHC) images of a breast tissue sample.

FIG. 7 is a schematic drawing showing exemplary steps of determining ormeasuring protein expression in a field of view.

FIG. 8 is a schematic drawing showing exemplary steps of determining ormeasuring protein heterogeneity.

FIG. 9 is a block diagram of an exemplary system implementing the breastcancer prognosis technologies described herein.

FIG. 10 is a flowchart of an exemplary computer-implemented methodimplementing the breast cancer prognosis technologies described herein.

FIG. 11 is a flowchart of an exemplary computer-implemented method foridentifying nuclei in a slide.

FIG. 12 is a block diagram of an exemplary system for field-of-viewscoring.

FIG. 13 is a block diagram of another exemplary system for field-of-viewscoring.

FIG. 14 is a block diagram of an exemplary classifier for determiningtrue positive stained nuclei.

FIG. 15 is a block diagram of an exemplary multi-stage classifier fordetermining true positive stained nuclei.

FIG. 16 is a flowchart of an exemplary method of determining a binnedHER2 score.

FIG. 17 is a block diagram of an exemplary system including aheterogeneity tool implementing the breast cancer prognosis technologiesdescribed herein.

FIG. 18 is a flowchart of an exemplary computer-implemented methodimplementing the breast cancer prognosis technologies described hereinvia determining a heterogeneity score for respective biomarkers.

FIG. 19 is a block diagram of an exemplary system including a differenceengine for calculating a heterogeneity score for a biomarker.

FIG. 20 is a flowchart of an exemplary computer-implemented method forcalculating a heterogeneity score for use in the breast cancer prognosistechnologies described herein.

FIG. 21 is a screenshot of an exemplary user interface for indicatingdigital fields of view within an image.

FIG. 22 is a flowchart of an exemplary digital pathologist workflowmethod.

FIG. 23 is a block diagram of an exemplary computing system in whichdescribed embodiments can be implemented.

FIG. 24 is a graph showing the ability of the disclosed methods toclassify a breast cancer sample as on that is more (progression) or less(no progression) aggressive. The cases shown in the graph are the samecases used to create the algorithm.

DETAILED DESCRIPTION Abbreviations and Terms

The following explanations of terms and methods are provided to betterdescribe the present disclosure and to guide those of ordinary skill inthe art in the practice of the present disclosure. The singular forms“a,” “an,” and “the” refer to one or more than one, unless the contextclearly dictates otherwise. For example, the term “comprising anantibody” includes single or plural antibodies and is consideredequivalent to the phrase “comprising at least one antibody.” The term“or” refers to a single element of stated alternative elements or acombination of two or more elements, unless the context clearlyindicates otherwise. As used herein, “comprises” means “includes.” Thus,“comprising A or B,” means “including A, B, or A and B,” withoutexcluding additional elements. Dates of GenBank Accession Nos. referredto herein are the sequences available at least as early as Dec. 28,2012.

Unless explained otherwise, all technical and scientific terms usedherein have the same meaning as commonly understood to one of ordinaryskill in the art to which this disclosure belongs. Although methods andmaterials similar or equivalent to those described herein can be used inthe practice or testing of the present disclosure, suitable methods andmaterials are described below. The materials, methods, and examples areillustrative only and not intended to be limiting.

Antibody: Immunoglobulin molecules and immunologically active portionsof immunoglobulin molecules, that is, molecules that contain an antigenbinding site that specifically binds (immunoreacts with) an antigen(such as ER, PR, Ki-67 or HER2). Exemplary antibodies includemonoclonal, polyclonal, and humanized antibodies.

A naturally occurring antibody (such as IgG, IgM, IgD) includes fourpolypeptide chains, two heavy (H) chains and two light (L) chainsinterconnected by disulfide bonds. As used herein, the term antibodyalso includes recombinant antibodies produced by expression of a nucleicacid that encodes one or more antibody chains in a cell (for example seeU.S. Pat. No. 4,745,055; U.S. Pat. No. 4,444,487; WO 88/03565; EP256,654; EP 120,694; EP 125,023; Faoulkner et al., Nature 298:286, 1982;Morrison, J. Immunol. 123:793, 1979; Morrison et al., Ann Rev. Immunol2:239, 1984).

The term antibody also includes an antigen binding fragment of anaturally occurring or recombinant antibody. Specific, non-limitingexamples of binding fragments encompassed within the term antibodyinclude Fab, (Fab′)₂, Fv, and single-chain Fv (scFv). Fab is thefragment that contains a monovalent antigen-binding fragment of anantibody molecule produced by digestion of whole antibody with theenzyme papain to yield an intact light chain and a portion of one heavychain or equivalently by genetic engineering. Fab′ is the fragment of anantibody molecule obtained by treating whole antibody with pepsin,followed by reduction, to yield an intact light chain and a portion ofthe heavy chain; two Fab′ fragments are obtained per antibody molecule.(Fab′)₂ is the fragment of the antibody obtained by treating wholeantibody with the enzyme pepsin without subsequent reduction orequivalently by genetic engineering. F(Ab′)₂ is a dimer of two FAb′fragments held together by disulfide bonds. Fv is a geneticallyengineered fragment containing the variable region of the light chainand the variable region of the heavy chain expressed as two chains.Single chain antibody (“SCA”) is a genetically engineered moleculecontaining the variable region of the light chain, the variable regionof the heavy chain, linked by a suitable polypeptide linker as agenetically fused single chain molecule. Methods of making thesefragments are routine in the art.

Binding affinity: Affinity of an antibody for an antigen, such as theaffinity of an antibody for an ER, PR, Ki-67 or HER2 peptide. Methods ofdetermining antibody affinity are known in the art, and includecalculation by a modification of the Scatchard method described byFrankel et al., Mol. Immunol., 16:101-106, 1979, measurement by anantigen/antibody dissociation rate, or by a competitionradioimmunoassay. A high binding affinity can be at least about 1×10⁻⁸M, at least about 1.5×10⁻⁸, at least about 2.0×10⁻⁸, at least about2.5×10⁻⁸, at least about 3.0×10⁻⁸, at least about 3.5×10⁻⁸, at leastabout 4.0×10⁻⁸, at least about 4.5×10⁻⁸, or at least about 5.0×10⁻⁸ M.

Breast cancer: Includes any tumor of the breast, such as tumors at,near, or inclusive of epithelial (carcinoma) or stromal (sarcoma) breasttissue. Ductal carcinoma in situ (DCIS) is a non-invasive neoplasticcondition of the ducts. Lobular carcinoma is not an invasive disease butis an indicator that a carcinoma may develop. Infiltrating (malignant)carcinoma of the breast can be divided into stages (I, IIA, IIB, IIIA,IIIB, and IV). See, for example, Bonadonna et al., (eds), Textbook ofBreast Cancer: A clinical Guide the Therapy, 3rd; London, Tayloy &Francis, 2006. DCIS is sometimes called Stage 0 breast cancer because itis not invasive. Exemplary invasive breast carcinomas include carcinomaNOS (not otherwise specified), lobular carcinoma, tubular/cribriformcarcinoma, mucinous (colloid) carcinoma, medullary carcinoma, papillarycarcinoma, and metaplastic carcinoma. An exemplary breast sarcoma isphyllodes tumor.

An early stage breast cancer is one that is stage I or II. A breasttissue sample, for example, that is ER positive, lymph node negative,and, in some examples, HER2 negative may also be characterized as earlystage breast cancer. Exemplary therapies for breast cancer includesurgery (e.g., removal of some or all of the tumor), hormone blockingtherapy (e.g., tamoxifen), radiation, cyclophosphamide plus doxorubicin(Adriamycin), taxane (e.g., docetaxel), and monoclonal antibodies suchas trastuzumab (Herceptin) or pertuzumab, or combinations thereof. Insome examples, the disclosed methods include administering one or moreof these therapies to a subject, such as one identified as having a moreaggressive tumor.

Control: A sample or standard used for comparison with an experimentalor test sample (such as a breast sample). In some embodiments, thecontrol is a normal sample obtained from a healthy patient (or pluralityof patients), such as a normal breast sample or plurality of samples. Insome examples, the control is a non-tumor tissue sample obtained from apatient diagnosed with breast cancer, such as normal breast tissue. Insome embodiments, the control is a known early stage breast cancersample (or plurality of samples), such as a sample known to be ER+, PR+,Ki-67+, and HER2−.

In some embodiments, the control is a historical control or standardreference value or range of values (such as a previously tested controlsample(s), such as a known breast cancer, normal breast sample, benignbreast sample, epithelium, or stroma). In some embodiments the controlis a standard value representing the average value (or average range ofvalues) obtained from a plurality of patient samples, such as knownnormal breast samples or known early breast cancer samples.

Control samples can be used for staining control. Such an approach canbe relevant to identifying the signal-to-noise ratio of the sample.

Contact: To bring one agent into close proximity to another agent,thereby permitting the agents to interact. For example, an antibody canbe applied to a microscope slide or other surface containing abiological sample, thereby permitting detection of proteins in thesample that are specifically recognized by the antibody.

Detect: To determine if an agent is present or absent, and can includedetermining a pattern. In some examples this can further includequantification. For example, use of an antibody specific for aparticular protein (e.g., Ki-67, ER, PR, or HER2) permits detection ofthe protein in a sample, such as a sample containing breast cancertissue. In particular examples, an emission signal from a detectablelabel (such as an increase in the signal if the target is present) isdetected.

Detection can be in bulk, so that a macroscopic number of molecules canbe observed simultaneously. Detection can also include identification ofsignals from single molecules using microscopy and such techniques astotal internal reflection to reduce background noise.

Estrogen receptor (ER): A member of the nuclear hormone family ofintracellular receptors is activated by 17β-estradiol. Estrogenreceptors are overexpressed in around 70% of breast cancer cases,referred to as “ER positive” (ER+).

H-Score: An indication of protein expression that weights stronglystained cells more heavily than weakly stained cells. For example, anH-score can indicate the percentage of cells staining weakly (e.g., 1+)plus two times the percentage of cells staining moderately (e.g., 2+)plus three times the percentage of cells staining strongly (e.g., 3+)(for example see Cuzick et al., J. Clin. Oncol. 29:4273-8, 2011,incorporated herein by reference) (also seewww.pathogenesys.com/html/semi-quantitative_ihc.html). Exemplary H-scorecalculation techniques are described herein.

Heterogeneity score: An indication of the amount of protein expressionheterogeneity of a biomarker in a sample, such as ER, HER2, Ki-67, or PRstaining in a breast cancer sample. The heterogeneity score provides ameasure of how different one FOV is from another FOV, for the samemarker.

Human epidermal growth factor receptor 2 (HER2): A member of the ErbBprotein family, which is a proto-oncogene located at the long arm ofhuman chromosome 17(17q11.2-q12). Approximately 25-30% of breast cancershave an amplification of the HER2/neu gene or overexpression of itsprotein product, referred to as “HER2 positive” (HER2+). HER2+ patientscan receive the monoclonal antibody trastuzumab (Herceptin) as a therapyfor breast cancer, and in some examples is used in combination with themonoclonal antibody Pertuzumab. Overexpression of HER2 in breast cancerhas been associated with increased disease recurrence and a worseprognosis.

Immunohistochemistry (IHC) combination score: A prognostic score basedon a number of IHC markers, wherein the number of markers is greaterthan one. IHC4 is one such score based on four measured IHC markers,namely ER, HER2, Ki-67, and PR in a breast cancer sample (for examplesee Cuzick et al., J. Clin. Oncol. 29:4273-8, 2011, and Barton et al.,Br. J. Cancer 1-6, Apr. 24, 2012, both herein incorporated byreference). In one example, an IHC4 score is calculated using, forexample, the following formula:IHC4=94.7×{−0.100ER ₁₀−0.079PR ₁₀+0.586HER2+0.240 ln(1+10×Ki67)}.

One skilled in the art will appreciate that other IHC combination scores(e.g., IHC3, IHC5, or the like) are possible.

Ki-67: A nuclear protein associated with cellular proliferation andribosomal RNA transcription. Inactivation of antigen Ki-67 leads toinhibition of ribosomal RNA synthesis. Ki-67 is used, for example, as amarker of proliferation.

Label: An agent capable of detection, for example by spectrophotometry,flow cytometry, or microscopy (such as light microscopy). For example,one or more labels can be attached to an antibody, thereby permittingdetection of the target protein. Exemplary labels include radioactiveisotopes, fluorophores, ligands, chemiluminescent agents, enzymes, andcombinations thereof. In one example the label is a quantum dot.

Normal cells or tissue: Non-tumor, non-malignant cells and tissue.

Output Prognosis Score: The result of combining and weighting IHCcombination (e.g., IHC4) scores and heterogeneity scores for a subject,from which breast cancer prognosis can be determined.

Progesterone receptor (PR or PgR): An intracellular steroid receptorthat specifically binds progesterone. Progesterone receptors areoverexpressed in some breast cancer cases, referred to as “PR positive”(PR+).

Prognose: The process of determining the likely outcome of a subjecthaving a disease (e.g., early stage breast cancer) in the absence ofadditional therapy. In one example, the disclosed methods allow forprognosis of a more aggressive form of an early stage breast cancer ifan output prognosis score above a threshold is detected. In contrast,prognosis of a less aggressive form of an early stage breast cancer isprognosed if an output prognosis score below a threshold is detected.For example, the prognosis can relate to predicting future events, suchas life expectancy (e.g., likelihood of survival in 1 year, 3 years or 5years), predicting the likely recurrence (either local or metastatic) ofbreast cancer (e.g., in 1, 3, or 5 years). The like term “prognosticate”is also used herein.

Quantify: To express as a numerical amount, whether an actual amount ora relative amount.

Sample: A biological specimen that may contain, for example, genomicDNA, RNA (e.g., mRNA), protein, or combinations thereof, obtained from asubject. Examples include, but are not limited to, fine needle aspirate,tissue biopsy, surgical specimen, and autopsy material. In one example,a sample includes breast tissue, such as that obtained during a needlebiopsy, lumpectomy, or mastectomy.

Staging cancer: A cancer, such as breast cancer, can be staged todescribe the extent or severity of a cancer based on the extent of theoriginal (primary) tumor and the extent of spread in the body. Breastcancer can be staged according to the TNM system (see the AJCC StagingManual), where T describes the size of the tumor and whether it hasinvaded nearby tissue, N describes any lymph nodes that are involved,and M describes metastasis (spread of cancer from one body part toanother).

The stages are as follows: Stage 0—Carcinoma in situ; Stage I—Tumor (T)does not involve axillary lymph nodes (N); Stage IIA—T 2-5 cm, Nnegative, or T<2 cm and N positive; Stage IIB—T>5 cm, N negative, or T2-5 cm and N positive (<4 axillary nodes); Stage IIIA—T>5 cm, Npositive, or T 2-5 cm with 4 or more axillary nodes; Stage IIIB—T haspenetrated chest wall or skin, and may have spread to <10 axillary N;Stage IIIC—T has >10 axillary N, 1 or more supraclavicular orinfraclavicular N, or internal mammary N; and Stage IV—Distantmetastasis (M).

Subject: Living multi-cellular vertebrate organisms, a category thatincludes human and non-human mammals, such as veterinary subjects. In aparticular example, a subject is one who had or is suspected of havinghad breast cancer, such as an early stage breast cancer.

Target molecule: A biomolecule whose detection or measurement isdesired, such as a breast cancer marker. Examples of target moleculesinclude ER, PR, Ki-67 and HER2.

Under conditions sufficient for: A phrase that is used to describe anyenvironment that permits the desired activity. An example includescontacting an antibody with a breast cancer sample sufficient to allowdetection of one or more target molecules (e.g., ER, PR, HER2, Ki-67) inthe sample and can include quantification of one or more targetmolecules in the sample.

Exemplary System Prognosing Breast Cancer

FIG. 1 is a block diagram of an exemplary system for carrying out thetechnologies described herein. In the example, an image acquisitiondevice 10, such as a slide scanner (e.g., iScan Coreo of Ventana MedicalSystems, Inc. or the like) is operable to accept a slide 5 prepared asdescribed herein and generate an image of the slide 5 for analysis asdescribed herein. In practice, a plurality of slides is used asdescribed herein.

The computing system 20 can include one or more input devices 23, one ormore displays 24, and one or more computers 21, which can execute animage processing application or platform as described herein.

Data, such as scanned images, can be stored, for example, remotely at aserver 12 and/or within a computer 21. Image acquisition can beseparately from (e.g., in a different system, by a different actor, orthe like) image processing.

In some implementations, a cloud-based or software-as-a-service scenariocan be implemented, in which the image processing application 22 residespartially or wholly outside the computer 21 (e.g., at the server 12 orone or more other servers).

Exemplary Methods of Prognosing Breast Cancer

It is shown herein that breast cancers, for example, early stage breastcancers, can be prognosed based on obtaining an immunohistochemistry(IHC) combination score (such as an IHC4) score and/or a heterogeneityscore. These two scores are combined and weighted, resulting in anoutput prognosis score. The resulting output prognosis score can be usedto prognosticate the patient with breast cancer. For example, if theoutput prognosis score is above a threshold value, this indicates thatthe breast cancer is more aggressive or more likely to recur, while anoutput prognosis score is below a threshold value indicates that thebreast cancer is less aggressive and less likely to recur. This allowsclinicians and patients the ability to make more appropriate treatmentand monitoring decisions. For example, the output prognosis may bebeneficial in determining an appropriate therapy (e.g., choosing betweenmonitoring, a mastectomy, lumpectomy, or a lumpectomy combined withchemotherapy).

Provided herein are methods of prognosing breast cancer, such as earlystage breast cancer, in a subject. In some examples, the breast canceris known to be estrogen receptor positive (ER+), progesterone receptorpositive (PR+), and human epidermal growth factor receptor 2 negative(HER2−) or human epidermal growth factor receptor 2 positive (HER2+).For example, the methods can be used to determine the likelyaggressiveness of the cancer, or the likelihood that the cancer willrecur, for example the likelihood that the cancer will recur within 5years.

Technologies herein can generate an IHC combination score and aheterogeneity score based on indicated fields of view on images of oneor more slides having breast cancer tissue taken from a subject. Thescores can then be combined to generate a prognosis score as describedherein. Various intermediary calculations and/or scores can be generatedbefore arriving at the IHC combination score or heterogeneity score asdescribed herein.

In an exemplary method, a breast cancer tissue sample is taken from asubject. In some examples, the breast cancer sample is an early stagebreast cancer sample, such as one that is HER2 negative (e.g., IHCstaining less than 2+ as described herein) and/or FISH amplified, ERpositive, and/or lymph node negative. Thus, a HER2 negative sample canbe one that (1) shows some IHC staining, such as staining that is lessthan 2+ or an H-score of <1, or (2) is amplified as indicated by FISHanalysis, but is one a clinician or a pathologist would conclude wasHER2 negative anyway. The sample may, alternatively, be HER2 positive.

In addition to FISH, other types of in situ hybridization (ISH) can beused, such as chromogenic in situ hybridization (CISH), dual colorchromogenic in situ hybridization (DISH), or the like.

FIG. 2 is a block diagram showing how images 32, 34, 36, and 38 ofslides stained for different proteins are used to generate a breastcancer recurrence prognosis score 58. Although different slides areshown, a tissue microarray approach as described herein can be employed,resulting in fewer slides. For example, a single slide can include aplurality of different tissue sections, which can each be analyzed for adifferent protein. In another example, a single tissue section, can beanalyzed for a plurality of proteins. A sample, for example a tissueblock, is divided into sections, for example, four sections, which mayor may not be adjacent or serial sections from the sample. Agentsspecific for the biomarkers of interest, for example, probes or stains,are applied to each section. In one example, an agent specific for theestrogen receptor ER is applied to one section, an agent specific forthe PR applied to another section, and the like. Exemplary agentsinclude antibodies and aptamers. Thus, the sample can be detectablylabeled with antibodies specific for each of ER, HER2, Ki-67, and PR.Images 32, 24, 36, and 38 of the sample sections are then acquired asdescribed herein. From the images 32, 34, 36, and 38, an H-score for ER41, a percent positivity for PR 42, a percent positivity for Ki-67 43,and a binned score for HER2 44 are calculated. The binned score 44 canbe converted into a binary score (e.g., 0 or 1) 45. The scores are thencombined as described herein to generate an IHC4 score 55.

The heterogeneity score for ER 51 relies on a percent positivitydetermination for ER 47, and the heterogeneity score for PR 52 relies ona percent positivity determination for PR 42. The heterogeneity scores51 and 52 can be combined as described herein to generate a combinedheterogeneity score 54.

The IHC4 score 55 and the combined heterogeneity score 54 can then becombined to generate a breast cancer recurrence prognosis score 58.

FIG. 3 provides an overview of an exemplary method. The method includesdetermining an IHC4 score via measured expression for each of ER, Ki-67,PR, and HER2 112, for example by determining or measuring a percentpositivity for each of PR and Ki-67, a binned score for HER2 (forexample on a typical 0 to 3 scale to represent the intensity, wherein 0is assigned to negative staining, 3 being assigned to very intenselystained samples), and an H-score for ER. The method also includesdetermining a heterogeneity score for at least ER and PR, and in someexamples also Ki-67 and HER2 114. The resulting IHC4 score 112 andheterogeneity score 114, are combined 116 to produce an output prognosisscore 118. Based on the output prognosis score, the breast cancer isprognosed 120.

In one example, each slide is scanned, and a digital image is generatedof each slide using any of the systems described herein. An IHCcombination score is generated by a computer-implemented method based onthe slides, and a heterogeneity score is generated for each slide (orsection of the slide being analyzed, such as a region containing atissue section for ER analysis). In an exemplary embodiment, the IHCcombination score is referred to as the “IHC4 score,” as four biomarkersare utilized to capture information. The IHC4 score can be combined withthe heterogeneity scores as described herein to generate a breast cancerrecurrence prognosis score.

In one example, to calculate the IHC4 score, the method includesselecting, in the image of each of the tissue section on one or moreslides from the breast cancer sample that has been labeled with an agent(such as an antibody), at least two different fields of view (FOV) foreach of an ER tissue section slide image, a HER2 tissue section slideimage, a Ki-67 tissue section slide image, and a PR tissue section slideimage. In an exemplary embodiment of the invention, three (3) FOVs areselected, for each slide or section, to compute the IHC4 score. Tumorousregions are chosen as fields of view.

FIG. 4A shows an exemplary set of slide images 180 and annotations bywhich fields of view (FOV) 181A, 181B, and 181C are selected (e.g., by apathologist) on respective of the slide images for calculating an IHCcombination score. As shown, the annotations can be independent with norelation between fields of view from one biomarker to the other. In theexamples, the fields of view are rectangular. However, as describedherein, other techniques can be used (e.g., the fields of view can beother known shapes or irregular shapes). In some examples, the field ofview is an area of interest, such as an anatomic region of interest(e.g., gland). In one example, the field of view is a whole slide orwhole tissue section.

Based on the fields of view, an IHC combination score is calculated.When calculating components for the IHC combination score, the fields ofview can be taken together (e.g., combined and/or consideredcollectively) for a particular biomarker. For example, H-score, percentpositivity, and HER2 binned score can be based on respective sets offields of view taken together (e.g., the H-score for ER is based on thecells observed in the fields of view). Other techniques are possible(e.g., averaging scores, voting for a score, or the like).

For example, the IHC combination score can be calculated using Dowsett'sIHC4 formula or a variation thereof:IHC4=94.7×{−0.100ER ₁₀−0.079PR ₁₀+0.586HER2+0.240 ln(1+10×Ki67)},wherein ER₁₀ is the H-score/30 for ER, wherein PR₁₀ is obtained bydividing the percentage of cells staining positive (e.g., with an upperlimit of 10% imposed on the percentage of cells) by 10 to generate avariable with a range of 0 to 10, wherein HER2 is the binary HER2 score(e.g., 0 if less than 2+, 1 otherwise), and Ki67 is the percentpositivity for Ki-67. In practice, ER₁₀ is scaled to a 0-10 range bydividing the H-score by 30 as shown in the formula.

For some of the fields of view, nuclei that are stained positive aredifferentiated from the nuclei that are stained negatively (e.g., notstained) in each field of view. Then, for the PR and Ki-67 slides, thepercent positivity is calculated (e.g., the total number of nuclei ofcells (e.g., malignant cells) that are stained positive in each field ofview in the digital image of a slide are summed and divided by the totalnumber of positively and negatively stained nuclei from each of thefields of view of a digital image) in a single slide as follows:Percent positivity=number of positively stained cells/(number ofpositively stained cells+number of negatively stained cells)In an exemplary embodiment, the percent positively is determinedmanually (e.g., without the use of a digital image, with the use ofimage analysis, or various other image analysis methods.

In an exemplary embodiment, nuclei are detected and classified aspositively stained nuclei or negatively stained nuclei via the nucleiidentification techniques described herein. For example, negativelystained cells can be differentiated from, for example, lymphocytes andstromal tissue. Such a technique can be superior to using a universalcolor threshold.

Additionally, the H-score (which reflects the intensity of the stainedcells and the number of stained cells for an individual slide) for theER slide (or tissue section) is calculated, for example. Such a scorecan be based on the FOVs taken together collectively. In exemplaryembodiments, a numerical scale (e.g., 0-300) is used for the H-score.For cells (e.g., in the fields of view) that have been positivelystained, the intensity is determined. Such intensity can be determinedvia an algorithm, compared to a threshold, and assigned a bin number.Such a bin number can be for example, 0, 1, 2, or 3. As a result, therewill be a count of nuclei (i.e., cells) in each bin. The bin counts canbe used to calculate the H-score.

The intermediate step of binning the nuclei can be re-used later whencalculating a heterogeneity score. The positive (e.g., brown)intensities (e.g., 1, 2, and 3) can be aggregated to determine anoverall positively stained nucleus count as needed (e.g., for theheterogeneity score for ER).

The calculation of the binary score for HER2 can include determining thebinned score (which is related to the completeness of the cell membranestaining and the intensity of stained cell membrane) for the HER2 slide(e.g., based on the fields of view). The binned score for the combinedFOVs will be a 0, 1, 2, or 3 as described herein. 0 or 1 are considerednegative (0 for the binary score), and 2 or 3 are considered positive (1for the binary score). Thus, a binary score is calculated for HER2.Alternatively, scores for the FOVs can be combined (e.g., averaged,average and rounded, or the like) to determine an overall score forHER2.

The IHC4 score can then be determined by utilizing the percentpositivity, H-score, and binary score described above. ER, HER2, Ki-67and PR protein expression can be detected or measured in each of theselected FOVs to assist in making these determinations. For example,protein expression can be examined or measured to determine or measure apercent positivity for each of Ki-67 and PR, a binned stain intensityscore for HER2, and an H-score for ER. Based on this information, animmunohistochemistry (IHC) combination score can be determined orcalculated according to the formula above.

Aside from the collective IHC (e.g., IHC4) combination score for thesample, heterogeneity, for example protein expression heterogeneity, canalso be calculated for respective biomarkers (e.g., ER, PER, HER2, andKi67). Heterogeneity determinations add prognostic value to the alreadyexisting value of the IHC combination (e.g., IHC4) score. In anexemplary embodiment, heterogeneity, for example, regional heterogeneityis determined for each biomarker (e.g., ER, PER, HER2, and Ki67). FIG.4B an exemplary set of slide images 185 showing annotations by whichfields of view 186A, 186B, and 186C are selected (e.g., by apathologist) on respective of the slide images for calculating aheterogeneity score. As shown, the annotations can be independent withno relation between fields of view from one biomarker to the other. Adifferent set of annotations can be used for heterogeneity than thoseused for the IHC combination score. In some cases, there can be partialoverlap between the FOV used for the IHC combination score and the FOVused for the heterogeneity scores (e.g., a field of view can be sharedbetween them for a biomarker).

In an exemplary embodiment, the heterogeneity is determined for a slide(or tissue section) to measure how different the various tumorousregions are from a reference, for example, each other. In an exemplaryembodiment, a pathologist selects a number of fields of view for a slide(or tissue section). In an embodiment, the pathologist selects threefields of view, each including tumorous regions with, for example,different percent positivities.

ER and PR protein heterogeneity is measured or detected using theindicated FOVs. For example, measuring protein heterogeneity can includedetermining a variability metric (VM) for each of ER, and PR, whereinVM=STD(PP(FS₁), PP(FS₂), . . . PP(FS_(N))). PP(FS) is the percentpositivity for each FOV, FS (e.g., the fields of view of a digital imageof a tissue sample that has been contacted with an agent specific for ERprobe, an agent specific for PR, or the like). Based on the variabilitymetric, a heterogeneity score for each of ER and PR is determined orcalculated. For example, the heterogeneity score for each of ER and PRcan be calculated using the following formula, where α=[0,1] (e.g., anumber ranging from 0 to 1) is a normalization factor, and S is anaverage percent positivity slide score as described herein:

$H = \left\{ \begin{matrix}{{\alpha*\frac{V\; M}{0.05}},{S < {10\%}}} \\{{\alpha*\frac{V\; M}{S}},{otherwise}}\end{matrix} \right.$Embodiments calculating heterogeneity for Ki-67 can use the same orsimilar formula as that used for ER and PR. The protein heterogeneityscore for HER2, which may be useful in some embodiments, can becalculated using the following formula:

$H = {\sum\limits_{\underset{i \neq j}{{\forall i},j}}{{{P\left( {FS}_{i} \right)} - {P\left( {FS}_{j} \right)}}}}$wherein P(FS) is the binned HER2 score (e.g., determined as describedherein) for each field of view, FS. If the binned scores for each fieldof view are equal (e.g., P(FS₁)=P(FS₂)=P(FS_(N))), then H=0. H thusindicates how different protein expression is in the different fields ofview for a biomarker.

In an exemplary embodiment, at least one of the heterogeneity scores isutilized to determine the output prognosis score. In an embodiment, theresulting heterogeneity scores for respective of the slides and the IHCcombination score are combined and weighted, thereby generating anoutput prognosis score. In an exemplary embodiment, the output prognosisscore is a factor or percentage (P1) times the IHC combination scoreplus a factor or percentage (P2) of a combined heterogeneity score,where P1 and P2 are greater than 0. For example, the output prognosisscore (PS) can be calculated by using the formula:PS=0.03114*IHC4+1.95119*combined heterogeneity scorewhere the combined heterogeneity score is equal to the square root (ERheterogeneity score+PR heterogeneity score). The resulting outputprognosis score if used to determine the prognosis of the patient havingthe breast cancer. For example, the method can prognosticate that thebreast cancer in the subject is likely to be aggressive or recur (e.g.,within 5 years) if the output prognosis score is above a certainthreshold or that the breast cancer in the subject is unlikely to beaggressive or recur if the output prognosis score is below thethreshold.

In some examples, the method can include obtaining a digitized image ofthe breast cancer sample (for example one that is on one or moremicroscope slides) that is detectably labeled for each of ER, HER2,Ki-67 and PR. For example, one or more digitized images can be obtainedfor each of ER, HER2, Ki-67 and PR. In some examples, the method furtherincludes selecting a subject prognosed as having a higher likelihood ofrecurrence, for example selecting the patient for more aggressivetherapy.

FIG. 5 provides further details on a particular embodiment of a methodin accordance with the technology. The method can also include acquiringor obtaining images (such as a digital image) of the breast cancersample stained to detect ER, HER2, Ki-67, and PR 108. At least two FOVfor each of ER, HER2, Ki-67, and PR are selected 110, for exampleselected and marked on a digital image. However, the number of FOVsselected may vary (e.g., there may be 2, 3, 4, 5, 6, 7, 8, 9 or 10FOVs). After selecting the FOVs 110, the method includes detecting ormeasuring ER, HER2, Ki-67, and PR expression in each FOV 111. Based onthis information, an IHC (e.g., IHC4) score is determined via theexpression for each of ER, Ki-67, PR, and Her2 112, for example bydetermining or measuring a percent positivity for each of PR, and Ki-67,a binary score based on a binned score for HER2 (for example on atypical 0 to 3 scale to represent the intensity, wherein 0 is assignedto negative staining, 3 being assigned to very intensely stainingsamples), and an H-score for ER. The method also includes detecting ormeasuring protein heterogeneity for each of ER and PR, and in someexamples also Ki-67 and HER2 113. Based on this information, aheterogeneity score for at least ER and PR, and in some examples alsoKi-67 and HER2, is determined 114. The resulting IHC4 score 112 andheterogeneity score 114, are combined to produce an output prognosisscore 118. Based on the output prognosis score, the breast cancer isprognosed 120.

FIG. 6 provides details on an exemplary method for obtaining oracquiring the IHC images of the breast tissue sample that is analyzedusing the methods provided herein. Methods of obtaining and stainingsamples are routine in the art. For example, the method can includeobtaining the breast tissue from a patient 210, for example from abreast biopsy. The resulting tissue is then fixed and embedded 212, forexample using formalin and paraffin. The fixed and embedded tissue canthen be sliced or sectioned and mounted onto a substrate 214, forexample mounted onto one or more glass microscope slides. In someexamples, one slide includes a plurality of tissue sections, such as atleast 2 or at least 4 sections. The tissue sections can then beincubated with appropriate antibodies (or other specific binding agents)to label ER, HER2, Ki-67, and PR proteins. For example, at least onebreast tissue section can be labeled with an ER-specific antibody,another breast tissue section can be labeled with a HER2 specificantibody, another breast tissue section can be labeled with a PRspecific antibody, and another breast tissue section can be labeled witha Ki-67 specific antibody 216. However, one skilled in the art willappreciate that a single tissue section can be labeled with more thanone antibody (or other specific binding agent), as long as such labeledproteins are distinguishable, for example by using differentlylabeled-secondary antibodies. After labeling the tissue sections, imagescan be obtained. For example, one or more images of the breast tissuesection labeled with the ER-specific antibody can be obtained, one ormore images of the breast tissue section labeled with the HER2-specificantibody can be obtained, one or more images of the breast tissuesection labeled with the PR-specific antibody can be obtained, and oneor more images of the breast tissue section labeled with theKi-67-specific antibody can be obtained 218.

FIG. 7 provides details on parameters that can be measured or determinedwhen detecting protein expression in each FOV. Such values can be usedto calculate the IHC (e.g., IHC4) score. As noted in FIG. 5, afterselecting the FOVs 110, the method includes detecting or measuring ER,HER2, Ki-67, and PR expression in each FOV 111. As shown in FIG. 7,detecting protein expression in each FOV can include determining ormeasuring a percent positivity for each of PR, and Ki-67 410,determining or measuring a binned score for HER2 412 as describedherein, and determining or measuring an H-score for ER 414. The scorescan be combined into an immunohistochemistry score as described herein.Combining can comprise converting the binned score for HER2 into abinary score for HER2.

FIG. 8 provides details on parameters that can be measured or determinedwhen measuring protein heterogeneity in each FOV. Such values can beused to calculate the heterogeneity score. As noted in FIG. 5, themethod can include detecting or measuring protein heterogeneity for eachof ER, HER2, Ki-67, and PR 113. As shown in FIG. 8, detecting proteinheterogeneity can include determining or measuring the variability ofpercent positivity in each FOV and normalizing the values, for at leastER and PR (and in some examples also Ki-67) 510. For example, avariability metric (VM) for each of ER and PR (and in some examplesKi-67) is calculated, wherein VM=STD(PP(FS₁), PP(FS₂), . . .PP(FS_(N))), and PP(FS) is the percent positivity for each field ofview, FS. As shown in FIG. 8, measuring protein heterogeneity for HER2can include determining or measuring the binned score for each FOV forHER2 and variation between binned scores 512 (e.g., using the binnedscore determined in 412 above, but incorporating at least one otherFOV).

Exemplary System Implementing the Technologies

FIG. 9 is a block diagram of an exemplary system 900 implementing thebreast cancer prognosis technologies described herein. In the example, aslide image processing tool 920 can accept a plurality of slide images912 and a plurality of fields of view 914 as input. The tool 920 outputsan indication 990 of whether cancer is likely to recur in the subject.As described herein, such an indication 990 can be based on an outputprognosis score.

The slides can depict protein expression for respective biomarkers in abreast cancer sample from a subject (e.g., detectably labeled withantibodies for the biomarkers as described herein).

The tool 920 can provide user interfaces for receiving selections of thefields of view (FOV) 914 or such functionality can be provided byanother tool or component.

The IHC combination score can be calculated (e.g., by the tool 920 oranother tool or component) based on the slide images and fields of viewwithin the slide images.

The tool 920 is operable to calculate one or more heterogeneity scores927 based on the slide images and fields of view within the slide imagesas described herein.

The slide image processing tool 920 can include a prognosis tool 930that accepts a calculated IHC combination score and the one or moreheterogeneity scores 927 as input and outputs an indication 990 ofwhether cancer is more aggressive, and thus more likely to recur in thesubject.

In practice, the systems shown herein, such as system 900 can be morecomplicated, with additional functionality, more complex inputs, and thelike. For example, additional functionality can compute the IHCcombination score, the heterogeneity score(s), or both, or such scorescan be provided by other software.

The system 900 and any of the other systems described herein can beimplemented in conjunction with any of the hardware components describedherein, such as the computing systems described below (e.g., processingunits, memory, and the like). In any of the examples herein, the inputs,outputs, and tools can be stored in one or more computer-readablestorage media or computer-readable storage devices. The technologiesdescribed herein can be generic to the specifics of operating systems orhardware and can be applied in any variety of environments to takeadvantage of the described features.

Exemplary Computer-Implemented Method Implementing the Technologies

FIG. 10 is a flowchart of an exemplary computer-implemented method 1000implementing the breast cancer prognosis technologies described hereinand can be implemented, for example, in the system shown in FIG. 9.

The method can be performed on one or more slide input images that arereceived for processing. For example, a slide image depicting a breastcancer sample from a subject detectably labeled with antibodies for abiomarker as described herein can be used.

At 1010, a plurality of digital fields of view are received within animage. As described herein, an indication of a field of view can bereceived via selections of displayed images (e.g., by tracing an outlineon the image for a field of view) or an indication of a boundary withinan image.

At 1012, protein heterogeneity is measured among the digital fields ofview as described herein (e.g., interregional heterogeneity isdetermined for the fields of view for a given slide image). For example,protein expression for a biomarker can be measured for the respectivefields of view, and the measurements compared for variability asdescribed herein. In practice, heterogeneity can be measured for aplurality of biomarkers (e.g., as a heterogeneity score). One or more ofER, PR, and the like (e.g., Ki-67, HER2, or the like) can be used.

When measuring protein heterogeneity for a second biomarker, a secondplurality of digital fields of view within a displayed image depicting abreast cancer sample from the subject detectably labeled with antibodiesfor a second biomarker can be received. Protein heterogeneity for thesecond biomarker can be measured (e.g., as a separate heterogeneityscore from the first).

At 1016, an IHC combination score for the subject is received. Any ofthe IHC combination scores described herein can be supported. The IHCcombination score can be for the same sample used for the heterogeneityanalysis, or a different sample from the same subject can be used.

At 1018, an output prognosis score is generated based at least on themeasured protein heterogeneity for the biomarker among the plurality ofdigital fields of view and the immunohistochemistry combination scorefor the subject. As described herein, an IHC combination score and oneor more heterogeneity scores for respective biomarkers can be generatedand used to calculate an output prognosis score.

At 1020, based on the output prognosis score, an indication of prognosis(e.g., indication 990 of whether cancer is more aggressive and thuslikely to recur in the subject) is output. For example, thresholds asdescribed herein can be used to select between or among categories ofprognosis indications; indicate a yes or no result; or the like.

The method 1000 and any of the other computer-implemented methodsdescribed herein can be performed by computer-executable instructions(e.g., causing a computing system to perform the method) stored in oneor more computer-readable media (e.g., storage or other tangible media)or stored in one or more computer-readable storage devices.

Exemplary Receiving of Field of View

In any of the examples herein, a field of view can be received as astored indication of an area (e.g., a set of pixels, a boundary, or thelike) within an image or as an annotation (e.g., drawing, tracing, orthe like) by a computer system operated by a pathologist with referenceto (e.g., on) an image (e.g., which is then stored for later use).

For example, annotation can be performed by a separate tool or softwareor incorporated into the tool or software that performs heterogeneityanalysis.

Exemplary Immunohistochemistry Combination Score

In any of the examples herein, an immunohistochemistry combination scorecan be combined with a heterogeneity score to yield a breast cancerrecurrence prognosis score. For example, the IHC (e.g., IHC4) scoredescribed herein or another immunohistochemistry combination scorecombining two or more biomarkers, for example, HER2, ER, PR, Ki-67, orthe like can be used. Such a score can be determined via the fields ofview indicated by a pathologist as described herein.

Heterogeneity can be analyzed for the biomarkers used to generate theIHC combination score as described herein and combined with the IHCcombination score to result in an output prognosis score.

Exemplary Image

In any of the examples herein, an image can be a digital image depictinga breast cancer sample from a subject detectably labeled with antibodiesfor a particular biomarker. In practice, such an image depicts a sectionof such a sample. Different sections can be used for differentbiomarkers.

The technologies can support a plurality of images for respective of thebiomarkers. Implementations can support multiple biomarkers in a singleimage (e.g., in a multiplex scenario).

When stored, an image can be represented as image data, pixels or voxelshaving, for example, color values, intensity values, or both. Imagepixel or voxels can be processed as described herein. For example,fields of view within the images can be analyzed to quantify proteinexpression within the field of view for a given biomarker.

Exemplary Fields of View

In any of the examples herein, a digital field of view can be an areawithin an image or an indication of such an area and is sometimes simplycalled a “field of view” herein. Such fields of view can compriseregions of interest as indicated by a pathologist. In someimplementations, slides can be compared to other slides, so the field ofview can be the entire slide. However, because the field of view istypically smaller than the entire digital image, it typicallycorresponds to an area that is smaller than the entire section beingimaged. In practice, such a digital field of view can be stored as aseparate image or indicated by a boundary (e.g., originally drawn by apathologist and stored electronically) with reference to a referenceimage (e.g., the image from which the field of view was derived).Portions of the image inside the boundary are considered within thefield of view, and portions of the image outside the boundary areconsidered outside the field of view. Portions on the boundary can beconsidered inside or outside as desired.

Typically, digital fields of view are contiguous regions of pixels orvoxels within the image as selected by a pathologist according to anappropriate protocol. Any arbitrary shape (e.g., rectangular,non-rectangular, square, elliptical, circular, traced shape, or thelike) or area of interest can be supported, and tools can be providedfor selecting particular shapes (e.g., a tracing tool, an ellipse tool,a circle tool, a square tool, a rectangle tool, or the like). A field ofview can comprise an anatomical structure of interest (such as a gland).

Different fields of view are typically selected for immunohistochemistrycombination scores and heterogeneity scores; however, it is possible forthere to be overlap between the two. In other words, fields of view foran immunohistochemistry combination score can be reused for aheterogeneity score and vice versa.

When storing, receiving, or outputting fields of view, a reference tothe digital field of view can be used instead of the actual field ofview data itself.

Exemplary Field of View Selection

In any of the examples herein, fields of view can be selected accordingto a protocol appropriate for the purpose. For example, for those fieldsof view used in an IHC (e.g., IHC4) score, the protocol specifies thatselected fields (e.g., 2, 3, 4, 5, 6, 7, or the like) be tumor regionsand be representative (e.g., similar looking) of protein expression ofthe biomarker within tumor tissue. For those fields of view used for aheterogeneity score, the protocol specifies that fields (e.g., 2, 3, 4,5, 6, 7, or the like) representing different (e.g., heterogeneous)levels of protein expression of the biomarker within tumor tissue are tobe selected. In some cases, a single field can be used for both purposes(e.g., one of the heterogeneous fields is also used in an IHCcombination score).

A user interface presented to the selecting user (e.g., a pathologist)can indicate for what purpose the fields of view are being selected andalso provide guidance regarding the protocol as appropriate.

Exemplary Protein Heterogeneity

In any of the examples herein, protein heterogeneity refers to thespatial variation of histochemical and molecular staining patterns, suchas the staining patterns for breast cancer biomarkers ER, HER2, Ki-67and PR in a breast cancer sample and is sometimes called simply“heterogeneity” herein. For example, heterogeneity increases with thevariability of level of biomarkers at different locations within asingle sample. Heterogeneity can be an indicator of the spatialvariation of tumor aggressiveness and/or growth patterns that can becorrelated with an aggregated clinical phenotype (e.g., a tumor likelyto recur).

It is shown herein that biological heterogeneity of ER and PR proteinexpression is correlated with the unpredictable recurrence of a fractionof early stage breast cancer patients.

Heterogeneity can be measured by a variability metric measuring howdifferent the protein expression levels among fields of view for thesame biomarker are (e.g., the variability of protein expressionmeasurements within fields of view for a single biomarker). Thus,interregional (e.g., inter-FOV) heterogeneity can be used.

A quantitative measure of interregional heterogeneity (e.g.,heterogeneity among the fields of view) can be calculated based on adeviation (e.g., standard deviation or other moment of the distribution)of protein expression measurement, PE (e.g., percent positivity), in thedifferent fields of view for a given biomarker (e.g., ER, PR, Ki-67, orthe like). Such a measurement can quantify how far apart (e.g., thespread of distribution) protein expression measurements are. Forexample, an exemplary heterogeneity calculation for a set of fields ofview having respective protein expression measurements PE(FS₁), PE(FS₂),. . . PE(FS_(N)) for a given biomarker in fields of view FS can becalculated as follows:Exemplary Heterogeneity (H)=σ(PE(FS ₁),PE(FS ₂), . . . PE(FS _(N)))The value can be normalized according to an average slide score (S),which can be the average of protein expression measurements for aparticular slide (e.g., the average of PE(FS₁), PE(FS₂), . . .PE(FS_(N))). If the average slide score (S) is below a threshold (e.g.,10% or the like), a substitute value (e.g., 5%) can be used as theaverage slide score. Normalization can be achieved by dividing theobserved deviation by the average slide score.

For biomarkers involving binned calculations (e.g., HER2), a sum of bindifferences between permutations of the fields of view can be used as ameasurement of heterogeneity. Heterogeneity can thus be calculated byaggregating binned score differences for different fields of view forthe marker. For example, heterogeneity for a set of fields of view (iand j) having respective protein expression binned scores (e.g., 0, 1,2, 3) P(FS₁), P(FS₂), . . . P(FS_(N)) for a given binned biomarker canbe calculated as follows:

$H = {\sum\limits_{\underset{i \neq j}{{\forall i},j}}{{{P\left( {FS}_{i} \right)} - {P\left( {FS}_{j} \right)}}}}$In the example, a single field of view has a single binned score. If allthe binned scores are equal, the score is 0.

As described herein, normalization can be done according to thebiomarker involved.

Exemplary Spatial Protein Heterogeneity

In any of the examples herein, spatial protein heterogeneity can bemeasured. Such heterogeneity can be categorized as geographical,regional, inter-glandular, intra-glandular, or the like.

For example, geographic heterogeneity can be measured by measuringvariation in protein expression at a geographic level: two separatetissue blocks more than a threshold distance (e.g., 2 inches) apart.

Regional heterogeneity can be measured by measuring variation in proteinexpression at a regional level: in the same tissue section, for example,between 0.25 and 2 inches apart, and different fields of view, forexample, at least distance of a 4× objective apart. Fields of view asdescribed herein can be used to measure such regional heterogeneity.

Inter-glandular heterogeneity can be measured by measuring variation inprotein expression at an inter-glandular level: for example, less than0.25 inches apart and within a 4× objective.

Intra-glandular heterogeneity can be measured by measuring variation inprotein expression at an intra-glandular level: for example, theformations in a 20× objective for regional and inter-glandularcategories.

Exemplary Alternative Heterogeneity Scores

In any of the examples herein, variability metrics (VM) other thanstandard deviation, σ, can be used to measure inter-region proteinheterogeneity. For example, inter-regional differences in proteinexpression measurements or a maximum thereof (e.g., when more than twofields of view) can be used. For example, an exemplary heterogeneitycalculation for a set of fields of view having respective proteinexpression (PE) measurements for fields of view FS₁, FS₂, FS₃ for agiven biomarker can be calculated as follows using an absolute value(ABS) function:VM=MAX(ABS(PE(FS ₁)−PE(FS ₂)),ABS(PE(FS ₁)−PE(FS ₃)),ABS(PE(FS ₂)−PE(FS₃)))

Such a calculation can also account for the range of variability. Forexample, the minimum inter-regional difference can also be taken intoaccount. An exemplary calculation can thus be:VM_(adj)=VM−MIN(ABS(PE(FS ₁)−PE(FS ₂)),ABS(PE(FS ₁)−PE(FS ₃)),ABS(PE(FS₂)—PE(FS ₃)))Further variations are possible.Exemplary Normalization of Heterogeneity Scores

In any of the examples herein, normalization can be applied toheterogeneity scores (e.g., a heterogeneity score for a biomarker). Forexample, normalization can be achieved by dividing the observeddeviation by the average slide score (e.g., for the measured biomarker)as described above.

Further normalization can be achieved based on the given biomarkerinvolved. For example, based on historical observation of theheterogeneity of protein expression for a given biomarker, a coefficientor other normalization technique can be used.

An exemplary heterogeneity calculation for with a normalization factor α(e.g., with range zero to one) for a variability metric VM (e.g.,standard deviation or the like) and S (e.g., an average percentpositivity slide score) thus can be:

$H = \left\{ \begin{matrix}{{\alpha*\frac{V\; M}{0.05}},{S < {10\%}}} \\{{\alpha*\frac{V\; M}{S}},{otherwise}}\end{matrix} \right.$

A per-biomarker normalization factor, a, can account for differences inimpact that heterogeneity of a specific biomarker has on the prognosisscore. As data for heterogeneity analysis is seen, such per-biomarkernormalization factors can be adapted.

In one example, a was 1.0 for ER and PR, and 0.75 for Ki-67. Such afactor can be determined via statistical methods to determineappropriate weightings for the biomarker(s) involved (e.g., to separatepatients into prognosis categories based on the resulting prognosisscore).

Exemplary Measurements of Protein Expression

In any of the examples herein, protein expression for a given biomarkercan be quantified by measuring a degree to which the protein isexpressed (e.g., in a field of view or collectively in fields of view).With reference to stained cells in a field of view, protein expressioncan be expressed as a percent of the cells in the field of view that arepositive stained. For example, a quantification, PP(FS), of proteinexpression for a field of view, FS, can be calculated to determinepercent positivity as follows:PP(FS)=(positively stained cell count in FS)/(total cell count in FS)In such a case, the total cell count can be a sum of the positivestained cell count and the negative stained cell count. In practice, acount of the positive stained cells can be determined, a count of thenegative stained cells can be determined, and based on the two, theprotein expression can be calculated. Because the quantificationindicates the percent of cells that are positive stained, such ameasurement is sometimes called “percent positivity” for a field of view(e.g., for a given biomarker).

Other techniques can be used to measure protein expression.

A binned score as described herein can be used to measure proteinexpression (e.g., for HER2).

Exemplary Determination of Percent Positivity

In any of the examples herein, percent positivity for a given field ofview can be determined via any number of techniques. The technologiesdescribed herein can make use of current and future-developed techniquesfor determining percent positivity.

Some such techniques count cell nuclei of positive stained cells andcount cell nuclei of negative stained cells. Such a technology canfilter out stromal and lymphocyte regions of the field of view. Ifdesired, information from an entire slide can be used when countingnuclei.

Thus, protein heterogeneity can be determined by determining percentpositivity measurements for respective fields of view for an imageassociated with a biomarker, where the percent positivity measurementsindicate a percent of cells in the respective fields of view that arepositive stained for the biomarker. The percent positivity measurementscan then be compared as described herein (e.g., to generate avariability metric).

Also, a percent positivity determination can include determiningintensity of staining and determining a count of nuclei for a pluralityof bins (e.g., 0, 1, 2, 3). Those nuclei in bin 0 can be considerednegative, and the others can be aggregated as the positively stainedcells. Having such bin information can be useful for other purposes,such as calculating an H-score or the like.

Exemplary Determination of Percent Positivity Implementation: NucleiIdentification

FIG. 11 is a flowchart of an exemplary computer-implemented method 1100for identifying nuclei in a slide. In the example, the method 1100 canuse information on the slide outside of the fields of view for theanalysis.

At 1110, a slide scan of a breast tissue section stained as describedherein is received. Such a scan can be performed at a firstmagnification (e.g., 20×, 40×, or the like) and/or resolution.

At 1120, the tissue region of the slide is identified. The slide scancan be separated into glass background and tissue region. Such analysiscan be performed at a second magnification (e.g., 1×, 2×, or the like)and/or resolution.

At 1130, a dominant color component analysis may be performed on thetissue region. For example, dominant colors can be extracted from thetissue region in the slide. Such analysis can be performed at a thirdmagnification (e.g., 5× or the like) and/or resolution.

At 1140, segmentation may be performed to segment the tissue region intodifferent labeled regions using color and texture features from multiplemagnifications.

At 1150, the nuclei in the slide and/or fields of view are detected andcounted (e.g., according to whether they are positive or negativestained). Scoring can thus be achieved.

Other techniques for identifying nuclei can be used. For example, anytechnique using color differentiation (e.g., between positive brown andnegative blue) and shape (e.g., morphological analysis) of candidatenuclei can be used to identify nuclei. In such techniques, morphologicalpost-processing can filter out stromal cells and stray blobs.

Exemplary Determination of Percent Positivity Implementation: FOVScoring

FIG. 12 is a block diagram of an exemplary system 1200 for field-of-viewscoring. In the example, an input field-of-view 1210 is shown. However,such analysis can be performed for multiple fields of view, one or moretiles of the slide, the entire slide, or any other input image.

Nuclei segmentation (e.g., identification of nuclei in an image) 1220can be performed to identify nuclei in the FOV. Any number of techniquescan be used to identify the nuclei.

Nuclei classification 1230 can then be performed to classify theidentified nuclei. Again, any number of techniques can be used to soclassify the nuclei as positive or negative stained.

At 1240, scoring is performed on the classified nuclei, resulting inpercent positivity for the biomarker. By identifying nuclei,corresponding cells are identified. Scoring can be performed for aparticular field of view, even if the analysis is performed on areasoutside of the field of view.

Exemplary Determination of Percent Positivity Implementation: FOVScoring

FIG. 13 is a block diagram of another exemplary system 1300 forfield-of-view scoring. In the example, an input field-of-view 1310 isshown. However, such analysis can be performed for multiple fields ofview, one or more tiles of the slide, the entire slide, or any otherinput image.

Nuclei segmentation (e.g., identification of nuclei in an image) 1320can be performed. At 1322, seed detection for the input image isperformed to locate seeds processed by the system. A seed is a pointthat lies within a candidate nucleus and serves as a starting point forlocalizing cell nuclei. Seed detection can use techniques such asoperating on a gradient image using a kernel based voting procedure.Foreground segmentation is performed at 1324. A foreground maskassociated with nuclei regions can be computed. Blob segmentation isperformed at 1326. Foreground segmentation and blob segmentation canmake use of global intensity variations from image to image. A blob-likerepresentation for each nucleus can be extracted.

Nuclei classification 1330 can be performed. At 1332, featureconstruction is performed. At 1336, classification is performed, withreference to a model 1334 from training Such a model can be specific tothe biomarker involved.

At 1340, scoring is performed, resulting in a percent positivity for thebiomarker. By identifying nuclei, corresponding cells are identified.Scoring can be performed for a particular field of view, even if theanalysis is performed on areas outside of the field of view.

Exemplary Determination of Percent Positivity Implementation:Classification

FIG. 14 is a block diagram of an exemplary classifier 1400 fordetermining true positive stained nuclei. In the example, candidatenuclei (e.g., incoming candidates) are processed by a classifier 1410,which separates into two classes: positives 1451 and negatives 1452. Itis possible that some incoming candidates do not classify as either, arefiltered out in advance, or the like. The classifier can determinenegatives 1452 to be those cells (e.g., associated with nuclei) that aredetermined to be cells that are not stained. Some candidates (e.g.,stromal tissue, lymphocytes, etc.) can be non-cells that are notincluded in (e.g., filtered out of) the negatives 1452 classification orthe positives 1451 classification.

The classifier can use features such as color features (e.g., mean,variance, and the like), tissue background color and context, shape(e.g., size, eccentricity, elongation), morphology, cell density, andthe like.

Exemplary Determination of Percent Positivity Implementation:Classification

FIG. 15 is a block diagram of an exemplary multi-stage linear binaryclassifier 1500 for determining true positive stained nuclei. In theexample, candidate nuclei (e.g., incoming blobs) are processed by aclassifier 1510, which separates into two preliminary classes:

1) brown (true positives) and faint junk blobs; and

2) negatives (e.g., unstained nuclei), lymphocytes, and stroma.

A classifier 1520 separates incoming candidates into stroma 1554 andnegatives and lymphocytes.

Another classifier 1530 separates incoming candidates into negatives1552 and lymphocytes 1553.

Still another classifier 1540 separates incoming candidates into brown(true positives) 1551 and faint brown junk blobs 1555.

The count of true positives 1551 and negatives 1552 can be used fordetermining percent positivity for a field of view.

The classifiers can use features such as color features (e.g., mean,variance, and the like), tissue background color and context, blob shape(e.g., size, eccentricity, elongation), morphology, cell density, andthe like.

Exemplary Determination of Binned HER2 Score

In any of the examples herein, a binned score can be generated for theHER2 biomarker. For an IHC combination score, the fields of view forHER2 can be considered collectively (e.g., analysis can be performed onthe digital fields of view collectively). For heterogeneity scores, abinned score can be generated for respective fields of view within animage of a tissue slide for the HER2 biomarker. In practice, such ascore determines the completeness of staining for the cell membranesurrounding a nucleus.

A binned score can comprise a single number (e.g., 0, 1, 2, or 3) forHER2 (e.g., either for the FOVs collectively or respective FOVs)determined as described herein.

Exemplary Binned HER2 Score Method

FIG. 16 is a flowchart of an exemplary method 1600 of determining abinned HER2 score and can be used in any of the examples herein in whicha binned HER2 score is used. Such a score can be generated collectivelyfor fields of view or for separate fields of view as described herein.

The method 1600 can be performed for a given image of a slide preparedand annotated as described herein for HER2. At 1610, tissue in the imageis segmented. For example, stromal and non-stromal areas can bedetermined.

At 1620, the nuclei can be detected as described herein, in, forexample, the non-stromal (e.g., gland) regions.

At 1630, nuclei are classified as stained or counter stained.

At 1640, the stained cell membrane in the image (e.g., whole image,FOVs, or the like) is detected.

At 1650, cells are scored by associating respective nuclei with thestained membrane around them. Based on presence of stained membranesurrounding the nuclei, a cell is classified into one of the followingtypes: non-stained (e.g., no stained membrane found around the nucleus),partially stained (e.g., the nuclei of the cell is partially surroundedby the stained membrane), completely stained (e.g., the nucleus iscompletely surrounded by the stained membrane). Parameters can be usedas thresholds for determining partial and completely stained. Ifdesired, such parameters can be adjusted by a user (e.g., a percentagesurrounding required to qualify as partial, a percentage surroundingrequired to qualify as complete). For example, cells with more than athreshold amount of (e.g., 90%) surrounding stained membrane can bedetermined to be “completely” stained.

In practice, for the detected cells (e.g., corresponding to detectednuclei), a cell can be assigned a staining completeness indicator (e.g.,indicating whether staining is complete or not, the degree ofcompleteness, or the like) and a staining intensity value based on pixelintensity for the stain (e.g., brown) color component (e.g., rangingfrom 0 to a maximum value, such as 100, 255, or the like). Suchinformation can then be analyzed to determine the HER2 score asdescribed herein.

Thus, for a plurality of cells appearing in the digital fields of viewfor HER2, respective staining completeness indicators and stainingintensity values can be determined. The staining completeness indicatorsand staining intensity values can then be analyzed via the conditions asdescribed herein.

For a plurality of nuclei appearing in the digital fields of view forHER2, the intensity of staining of cell membrane surrounding respectiveof the nuclei can be determined.

At 1660, the fields of view (e.g., collectively or respectively) arescored based on scores of cells in the field of view. For example, thefollowing conditions can be used (e.g., if the field of view qualifiesfor a higher score, processing need not be done for the lower scores):

Assign Condition Score Responsive to determining percentage ofcompletely stained 3+ cells > a threshold (e.g., 30%) Responsive todetermining (The percentage of completely 2+ stained cells > a threshold(e.g., 10%)) OR (percentage of completely stained cells is > 0% ANDmembrane median intensity is less than strong intensity threshold)Responsive to determining (percentage of partially stained 1+ cells >0%) OR (percentage of completely stained cells > 0% AND percentage ofcompletely stained cells < a threshold (e.g., 10%) AND membrane medianintensity >= strong intensity threshold AND membrane median intensity <weak intensity threshold) Responsive to determining the above conditionsare not met 0  (e.g., otherwise)As described, membrane median intensity can be compared with a strongintensity threshold. It can also be determined, for a plurality ofnuclei appearing in digital fields of view for HER2, whether cellmembrane surrounding the nuclei are completely stained.

The field of view binned scoring for HER2 can thus be achieved. If abinary score is desired, the intermediate step of determining binnedscore can be omitted or combined into the process.

Exemplary Staining Intensity

In any of the examples herein, positive staining (e.g., of cellmembrane) can be quantified by an average brownness. For example, anaverage brownness on the membrane can be determined by the luminancescalar value (e.g., 0-255), which can be computed from the RGB pixelvalues of the membrane region. RGB values can be converted to luminancevalue (L) via an RGB to L*a*b* conversion technique. The L value can beconverted from a 0 to 100 scale to a 0 to 255 scale.

An average brownness for a cell can be calculated by averaging L overthe membrane pixels. The average brownness can be used as the medianintensity (e.g., of cell membrane) as described above.

Exemplary Nuclei Classification

In any of the examples herein, nuclei can be classified for purposes ofcalculating the binned HER2 score. For example, cells can be classifiedas stained and counter stained, based on saturation, intensity, and redand blue color information in each cell from the image. Saturation andintensity information can be used to distinguish dark gray cells so theycan be classified as stained instead of non-stained.

For example, the following rules can be used:

A. StainPercentage1=100*(R−G)/(R+1)

B. StainPercentage2=100*(R−B)/(R+1)

C. If StainPercentage1>PixellevelStain % andStainPercentage2>PixellevelStain %, then Pixel=Stained

D. If Saturation<45 and Intensity<128, then Pixel=Stained

E. If Saturation<128 and Intensity<55, then Pixel=Stained

F. If Saturation<255 and Intensity<=30, then Pixel=Stained

G. If Intensity<=20, then Pixel=Stained

Using the pixel classification information, each nuclei can beclassified as stained or non-stained based on the percentage of thestained pixels within each nucleus. For example, the following rules canbe used:

A. If the cell level threshold>% of the stained pixels within eachnuclei, then classify the cell as stained

B. Else, classify it as non-stained

The nuclei objects can then be filtered based on size.

Exemplary Binned HER2 Score Controls

Four cell line controls can be used to determine whether binned HER2scores are being determined as described herein. When processed andstained as described herein, the cell lines should stain as described inthe following table. The cell lines are available from Ventana MedicalSystems, Inc. as catalog #781-2991.

HER2 IHC Score Cell Line HER2 Gene Copy # 0  MCF-7 1.7 1+ T47D 2.9 2+MDA-MB-453 5.2 3+ BT-474 18.9

The HER2 Gene Copy # was determined as an average of three lots ofPATHWAY HER-2 4 in 1 control slides determined using PathVysion® HER2Probe.

Additional information about HER2 scoring can be found in documentationfor “PATHWAY® anti-HER-2/neu (4B5) Rabbit Monoclonal Primary Antibody”Catalog Number 790-2991 available from Ventana Medical Systems, Inc.,which is hereby incorporated by reference herein.

Exemplary H-Score Calculation

In any of the examples herein, an H-score can be calculated from a fieldof view or fields of view collectively via automated techniques. Forexample, nuclei can be identified, and then cells (e.g., correspondingto the nuclei) can be categorized (e.g., binned) into four bins: 0, 1(weakly stained), 2 (moderately stained), or 3 (strongly stained). Sucha technique can be achieved by determining the average brownness of acell or clump of cells (e.g., blob).

By examining color components (e.g., RGB or the like), the averagebrownness can be compared against thresholds to determine whether a cellis not stained (0), staining weakly (1+), staining moderately (2+), orstaining strongly (3+). The counts of the cells in the bins can bemaintained, and at the conclusion of the counting, an H-score can becalculated based on the percentage of cells in each bin as describedherein.

For example, given the counts of cells in a plurality of bins associatedwith respective staining intensities (e.g., including a bin for zerointensity), the H-score can be calculated by summing, for the bins, theproduct of the percentage (e.g., percentage of cell count, such as thenumber of cells in a bin divided by the total number of cells) of cellsin a bin by the respective intensity level associated with the bin. Inan arrangement that has four bins, 0-3, the H-score will range from0-300. The maximum score in such an arrangement is achieved if all(100%) of the cells are of intensity 3 (e.g., 0×0+0×1+0×2+100×3=300).Thus, the percentage can be treated as a whole number rather than afraction (e.g., 50%=50). The intensity level associated with a bin canbe the numerical portion of an intensity designator (e.g., 2+ is treatedas 2).

Exemplary Breast Cancer Recurrence Prognosis Score

In any of the examples herein, a breast cancer recurrence prognosisscore can be generated based on an IHC combination score andheterogeneity scores for one or more biomarkers. Such a prognosis scoreis sometimes simply called an “output prognosis score.”

The score can be generated by combining the IHC combination scores andheterogeneity scores. For example, the heterogeneity scores can beadded, multiplied, or otherwise combined and then operations (e.g.,square root, exponent, coefficients, etc.) applied to form aheterogeneity component to the score.

The immunohistochemistry combination score can likewise be adjusted(e.g., square root, exponent, coefficients, etc.) to form animmunohistochemistry combination component to the score.

The two components can then be combined (e.g., added, multiplied, or thelike).

One particular technique for calculating the output prognosis scorefollows:F _(IHC)*IHC4+F _(Het)*√{square root over (Het_(ER)+Het_(Pr))}where F_(IHC) is an immunohistochemistry combination coefficient,F_(Het) is a heterogeneity coefficient, and Het_(ER) is a heterogeneityscore for ER and Hetp_(R) is a heterogeneity score for PR. In oneimplementation, F_(IHC)=0.03114 and F_(HET)=1.95119. However, thetechnologies can support adjustment of the coefficients as desired. Forexample, statistical analysis can indicate appropriate coefficients thatcan change in light of accumulated data (e.g., to divide patients intocategories as described herein).Exemplary Indication of Prognosis

In any of the examples herein, an indication of prognosis can comprisean output prognosis score itself, a category (e.g., red, yellow, green)based on thresholds, or the like. The indication can be of a form thatindicates whether or not (or how likely) cancer is predicted to recur,based on the inputs.

The indication of breast cancer recurrence can be chosen (e.g., from aplurality of categories) based on one or more stored thresholds for thebreast cancer recurrence prognosis score. For example, if the scoremeets a threshold, a first indication can be chosen (e.g., recurrenceprognosis); if the score does not meet the threshold, a secondindication can be chosen (e.g., non-recurrence prognosis).

Such categories can comprise a time component as well (e.g., expected torecur within 5 years) or the like.

Multiple indications can be supported (e.g., one category for whethercancer is expected to recur within 1 year, another category for whethercancer is expected to recur within 5 years, and the like).

Exemplary Implementation in Imaging Platform

Although the technologies can be implemented on a standalone basis, thetechnologies described herein can be implemented in a multi-functiondigital pathology platform that aids users in analysis of slide imagedata. For example, the technologies can be integrated into the Virtuosoimage management software of Ventana Medical Systems, Inc., orcomparable solutions.

Such a platform can serve as a front end for the technologies. Slideimages and information collected about them (e.g., fields of view) viathe digital pathology platform can be used as inputs to the technologiesdescribed herein.

Such a platform can allow pathologists the flexibility to work on cases,specimens, and images in any desired order. Slide images can beannotated, and a variety of other functionality can be made available.

The platform can be implemented in a thin-client (e.g., software as aservice, cloud computing, or the like) scenario as desired.

Exemplary System with Heterogeneity Tool Implementing the Technologies

FIG. 17 is a block diagram of an exemplary system 1700 including aheterogeneity tool 1720 implementing the breast cancer prognosistechnologies described herein. In the example, a heterogeneity tool 1720accepts a plurality of digital fields of view 1711A-N, 1714A-N forrespective biomarkers as input and, with the assistance of an optionalnormalizer 1725, outputs heterogeneity scores 1731, 1734 for respectiveof the biomarkers.

The features of FIG. 9 can be intermingled or incorporated as desired.

A prognosis tool 1740 accepts the heterogeneity scores 1731, 1734 andIHC combination score 1750 as input and, with reference to a prognosisthreshold 1745, outputs an indication 1790 of whether cancer is likelyto recur in the subject.

Exemplary Computer-Implemented Method Implementing the Technologies ViaHeterogeneity Score for Respective Biomarkers

FIG. 18 is a flowchart of an exemplary computer-implemented method 1800implementing the breast cancer prognosis technologies described hereinvia determining a heterogeneity score for respective biomarkers and canbe implemented, for example, in the system shown in FIG. 17.

At 1810, a plurality of digital fields of view are received forrespective biomarkers.

At 1812, based on the digital fields of view, a heterogeneity score forrespective of the biomarkers is determined.

At 1816, an IHC combination score and the heterogeneity scores arecombined into a prognosis score.

At 1818, an indication of the prognosis (e.g., an indication 1790 ofwhether cancer is likely to recur in the subject) is output based on theprognosis score.

Exemplary IHC Combination Score Calculation

A computer-implemented method can calculate an immunohistochemistrycombination score for a subject by receiving respective pluralities ofdigital fields of view within respective images depicting a breastcancer sample detectably labeled with respective biomarker antibodies(e.g., for a plurality of biomarkers as described herein).

Then, the percent positivity for a plurality of the biomarkers can bedetermined as described herein.

Then, the immunohistochemistry combination score can be calculated. Suchcalculation can comprise combining the percent positivity for onebiomarker with the percent positivity for a second biomarker.

An immunohistochemistry combination score can then be output.

The biomarkers can comprise HER2, and calculating the IHC combinationscore can comprise calculating a HER2 score (e.g., binned, binary, orthe like) as described herein.

Exemplary Biomarkers

In the example of FIG. 17, the biomarkers ER and PR are used fordetermining regional heterogeneity. In practice, the heterogeneity ofdifferent combinations of biomarkers can be calculated and combined withan IHC combination score to generate a prognosis score.

Biomarkers that can be used can include combinations of one or more ofthe biomarkers used as part of the IHC combination score (e.g., ER, PR,Ki-67 and HER2). Any of the other examples herein can be modifiedaccordingly (e.g., to obtain fields of view, calculate heterogeneityscores, or the like).

Exemplary Multiplexing

In any of the examples herein, biomarkers can be measured in a multiplexscenario. For example, as described herein, a single tissue section canbe labeled with more than one antibody (such as two antibodies specificfor two different biomarkers, etc.), as long as such labeled proteinsare distinguishable, for example by using differently labeled-secondaryantibodies.

Exemplary Tissue Microarray Techniques

In any of the examples herein, tissue microarray techniques can beapplied to accomplish tissue staining. For example, a plurality oftissue samples can be applied to a single substrate, and a plurality ofstaining agents (e.g., labeling antibodies) can be applied to thesamples (e.g., a different agent per tissue sample). The techniquesdescribed herein can then be used to determine one or more scores asdescribed herein (e.g., based on images of the detectably labeled tissuesamples).

Exemplary Thresholds

In any of the examples herein, thresholds can be derived by statisticalevaluation. For example cut-points can be determined using training andtesting to validate an optimal cut-point that best defines high versuslow risk patients.

Exemplary System with Difference Engine Implementing the Technologies

FIG. 19 is a block diagram of an exemplary system 1900 including adifference engine 1940 for calculating a heterogeneity score for abiomarker. The features of FIG. 9 and/or FIG. 17 can be incorporated orintermingled as desired.

In the example, a field of view image analyzer 1920 accepts digitalfields of view 1711A-N (e.g., for a single biomarker) as input andoutputs percent positivity measurements 1931A-N for respective of thefields of view.

The difference engine 1940 accepts the percent positivity measurements1931A-N as input and, with assistance of an optional normalizer 1945,outputs a heterogeneity score 1731 for the biomarker.

Exemplary Computer-Implemented Method for Calculating HeterogeneityScore

FIG. 20 is a flowchart of an exemplary computer-implemented method 2000for calculating a heterogeneity score for use in the breast cancerprognosis technologies described herein and can be implemented, forexample, in the system shown in FIG. 19.

At 2010, a plurality of digital fields of view for a given biomarker arereceived. Such an image can depict a breast cancer sample from a subjectdetectably labeled with antibodies for a biomarker as described herein.

At 2012, protein expression for the biomarker in the digital fields ofview is measured. For example, percent positivity measurements forrespective of the biomarkers are received or determined.

At 2016, heterogeneity of the measured protein expression for thebiomarker is measured among the plurality of digital fields of view. Forexample, a heterogeneity score is calculated as the variability betweenthe percent positivity measurements appearing in the different fields ofview (e.g., for a single biomarker).

The heterogeneity score (e.g., preliminary heterogeneity score orvariability metric) can be normalized as described herein to adjust theheterogeneity score.

At 2020, the heterogeneity score is output for the biomarker (e.g., foruse in the technologies described herein).

Exemplary User Interface

FIG. 21 is a screenshot of an exemplary user interface 2100 forindicating digital fields of view within an image. A user interface cantake the form of a window, screen, pane, or the like presented to a userfor accepting input (e.g., indications of fields of view on a displayedslide image).

In the example, a pathologist has selected three fields of view 2120A,2120B, and 2120C on a displayed slide for a particular biomarker (e.g.,ER) for heterogeneity purposes. Independent fields of view can beselected for different biomarkers. Further independent fields of viewcan be selected for heterogeneity purposes and immunohistochemistrycombination score purposes.

Exemplary Pathologist Workflow

FIG. 22 is a flowchart of an exemplary digital pathologist workflowcomputer-implemented method 2200. A digital pathologist workflow can besupported to facilitate field of view selection on (e.g., within)images.

At 2210, via a user interface requesting fields of view for an IHCcombination score, a plurality of fields of view for the IHC combinationscore are received within a plurality of slide (or tissue) imagesdisplayed for determining the IHC combination score. Such a userinterface can comprise a plurality of slide (or tissue) images depictinga breast cancer sample from a subject detectably labeled with antibodiesfor each of ER, HER2, Ki-67, and PR. Receiving the plurality of fieldsof view for the IHC combination score can comprise receiving a pluralityof fields of view for each of ER, HER2, Ki-67, and PR. For example, aseries of slides (or tissue sections) can be presented, and thepathologist can indicate a plurality of fields on the respective slides(e.g., FOVs for a first slide, FOVs for a second slide, etc.) (or aplurality of fields for each tissue section). Such an indication can beaccomplished by receiving annotations (e.g., drawn areas) on a displayedimage.

At 2212, via a user interface requesting fields of view for aheterogeneity score, a plurality of fields of view for a heterogeneityscore are received within a plurality of slide (or tissue) imagesdisplayed for determining the heterogeneity score. Such a user interfacecan comprise a plurality of slide (or tissue) images depicting a breastcancer sample from the subject detectably labeled with antibodies foreach of ER and PR. Receiving the plurality of fields of view for the IHCcombination score can comprise receiving a plurality of fields of viewbiomarkers, such as for each of ER and PR. Such receiving can beaccomplished by receiving annotations (e.g., drawn or identified areas)on a displayed image.

At 2216, protein expression values for the fields of view for the IHCcombination score are measured, and an IHC combination score iscalculated therefrom.

At 2218, protein expression values for the fields of view for theheterogeneity score are measured, and a heterogeneity score iscalculated therefrom. One or more other heterogeneity scores forrespective other biomarkers can be similarly obtained.

At 2220, the IHC combination score and the one or more heterogeneityscores are combined into a breast cancer recurrence prognosis score.

At 2222, an indication of breast cancer recurrence prognosis based onthe breast cancer recurrence prognosis score is displayed (e.g., forconsideration by a viewing pathologist).

Exemplary Field of View Selection

In any of the examples herein, fields of view for an IHC combinationscore can be selected to be representative of the entire slide. A userinterface presented to the pathologist can so indicate.

In any of the examples herein, fields of view for a heterogeneity scorecan be selected to be representative of a region on the slide (e.g., andthus chosen to emphasize differences between regions). A user interfacepresented to the pathologist can so indicate.

Exemplary Further Description

A. Biological Samples

Methods of obtaining a biological sample from a subject are known in theart. For example, methods of obtaining breast tissue or breast cells areroutine. For example, a sample from a tumor that contains cellularmaterial can be obtained by surgical excision of all or part of thetumor, by collecting a fine needle aspirate from the tumor, as well asother methods known in the art.

Samples may be fresh or processed post-collection. In some examples,processed samples may be fixed (e.g., formalin-fixed) and/or wax- (e.g.,paraffin-) embedded. Fixatives for mounted cell and tissue preparationsare well known in the art and include, without limitation, 95% alcoholicBouin's fixative; 95% alcohol fixative; B5 fixative, Bouin's fixative,formalin fixative, Karnovsky's fixative (glutaraldehyde), Hartman'sfixative, Hollande's fixative, Orth's solution (dichromate fixative),and Zenker's fixative (see, e.g., Carson, Histotechology: ASelf-Instructional Text, Chicago: ASCP Press, 1997). Thus, the samplecan be a fixed, wax-embedded breast cancer tissue sample, such as afixed, wax-embedded early breast cancer tissue sample. In some examples,the sample is a breast tissue section stained with hematoxylin and eosin(H&E). In some examples, the sample is a breast tissue section labeledwith primary antibodies specific for ER, HER2, Ki-67 and PR, which maybe labeled directly or indirectly (e.g., with a labeled secondaryantibody), which in some examples is further stained with H&E.

In some examples, the sample (or a fraction thereof) is present on asolid support. Solid supports bear the biological sample and permit theconvenient detection of components (e.g., proteins) in the sample.Exemplary supports or substrates include microscope slides (e.g., glassmicroscope slides or plastic microscope slides), coverslips (e.g., glasscoverslips or plastic coverslips), tissue culture dishes, multi-wellplates, membranes (e.g., nitrocellulose or polyvinylidene fluoride(PVDF)) or BIACORE™ chips.

B. Breast Cancer Biomarkers

1. Estrogen Receptor (ER) (OMIM: 133430)

The human estrogen receptor (ER or ESR) has two different forms,referred to as α (ESR1) and β (ESR2). ER a is the form found in breastcancer cells. ER a sequences are publicly available, for example fromGenBank® (e.g., accession numbers NP_001116214.1, NP_001116213.1, andP03372.2 (proteins), and NM_000125.3, NM_001122741.1, and NM_178850.2(nucleic acids)).

The estrogen receptor is a ligand-activated transcription factorcomposed of several domains important for hormone binding, DNA binding,and activation of transcription. Alternative splicing results in severalESR1 mRNA transcripts, which differ primarily in their 5′ untranslatedregions.

Antibodies for detecting ER expression are publicly available, such asfrom Santa Cruz Biotechnology (Santa Cruz, Calif.), Thermo ScientificPierce Antibodies (Rockford, Ill.), GeneTex (Irvine, Calif.), ARPAmerican Research Products, Ventana Medical Systems, Inc. (Tucson,Ariz.) or Abcam (Cambridge, Mass.), for example Cat. Nos. sc-71064,sc-73562, and sc-7207 from Santa Cruz Biotechnology, MA1-310 from PierceAntibodies, 790-4325 (clone SP1) from Ventana, or ab2746, ab27614, orab37438 from Abcam.

2. Human Epidermal Growth Factor Receptor 2 (HER2) (OMIM 164870)

The human HER2 gene is located on chromosome 17 (17q21-q22). HER2sequences are publicly available, for example from GenBank® (e.g.,accession numbers NP_001005862.1, P04626.1, and NP_004439.2 (proteins)and NM_001005862.1 and NM_001982.3 (nucleic acids)).

Amplification or over-expression of HER2 plays a role in thepathogenesis and progression of certain aggressive types of breastcancer and is an important biomarker and target of therapy for thedisease.

Antibodies for measuring HER2 expression are publicly available, such asfrom Santa Cruz Biotechnology (Santa Cruz, Calif.), Thermo ScientificPierce Antibodies (Rockford, Ill.), GeneTex (Irvine, Calif.), ARPAmerican Research Products, Ventana Medical Systems, Inc. (Tucson,Ariz.) or Abcam (Cambridge, Mass.), for example Cat. Nos. 790-2991(clone 4B5) from Ventana, sc-08, sc-81528, and sc-136294 from Santa CruzBiotechnology, and ab2428, ab16901 or ab36728 from Abcam.

3. Ki-67 (OMIM: 176741)

The human Ki-67 gene is located on chromosome 10. Ki-67 sequences arepublicly available, for example from GenBank® (e.g., accession numbersNP_001139438.1, P46013.2 and NP_002408.3 (proteins) and NM_001040058.1and NM_001145966.1 (nucleic acids)).

Ki-67 is a marker that can be used to determine the growth fraction of agiven cell population. The fraction of Ki-67-positive tumor cells (theKi-67 labeling index) is often correlated with the clinical course ofcancer, such as breast cancer. Thus, Ki-67 can be used to prognosepatient survival and tumor recurrence.

Antibodies for measuring Ki-67 expression are publicly available, suchas from Santa Cruz Biotechnology (Santa Cruz, Calif.), Thermo ScientificPierce Antibodies (Rockford, Ill.), GeneTex (Irvine, Calif.), ARPAmerican Research Products, Ventana Medical Systems, Inc. (Tucson,Ariz.) or Abcam (Cambridge, Mass.), for example Cat. Nos. 790-4286(clone 30-9) from Ventana, sc-101861 (clone MIB-1), sc-15402, andsc-23900 from Santa Cruz Biotechnology, and ab15580, ab16667 or ab8191from Abcam.

4. Progesterone Receptor (PR) (OMIM: 607311)

The human PR gene (also known as nuclear receptor subfamily 3, group C,member 3, NR3C3) is located on chromosome 11q22. PR sequences arepublicly available, for example from GenBank® (e.g., accession numbersNP_000917.3, AAA60081.1, and AAS00096.1 (proteins) and NM_001202474.1and NM_000926.4 (nucleic acids)).

Amplification or over-expression of PR has been shown in some breastcancers.

Antibodies for measuring PR expression are publicly available, such asfrom Santa Cruz Biotechnology (Santa Cruz, Calif.), Thermo ScientificPierce Antibodies (Rockford, Ill.), GeneTex (Irvine, Calif.), ARPAmerican Research Products, Ventana Medical Systems, Inc. (Tucson,Ariz.) or Abcam (Cambridge, Mass.), for example Cat. Nos. 790-2223(clone IE2) from Ventana, sc-810, sc-811, and sc-539 from Santa CruzBiotechnology, and ab2765, ab2764 or ab68195 from Abcam.

5. Variant Sequences

In addition to the specific ER, HER2, Ki-67 and PR publicly availablesequences provided herein, one skilled in the art will appreciate thatvariants of such sequences may be present in a particular subject. Forexample, polymorphisms for a particular gene or protein may be present.In addition, a sequence may vary between different organisms. Inparticular examples, a variant sequence retains the biological activityof its corresponding native sequence. For example, a ER, HER2, Ki-67 orPR sequence present in a particular subject may can have conservativeamino acid changes (such as, very highly conserved substitutions, highlyconserved substitutions or conserved substitutions), such as 1 to 5 or 1to 10 conservative amino acid substitutions. Exemplary conservativeamino acid substitutions are shown in Table 1.

TABLE 1 Exemplary conservative amino acid substitutions Highly ConservedConserved Very Highly - Substitutions Substitutions Original Conserved(from the Blosum90 (from the Blosum65 Residue Substitutions Matrix)Matrix) Ala Ser Gly, Ser, Thr Cys, Gly, Ser, Thr, Val Arg Lys Gln, His,Lys Asn, Gln, Glu, His, Lys Asn Gln; His Asp, Gln, His, Lys, Arg, Asp,Gln, Glu, His, Ser, Thr Lys, Ser, Thr Asp Glu Asn, Glu Asn, Gln, Glu,Ser Cys Ser None Ala Gln Asn Arg, Asn, Glu, His, Arg, Asn, Asp, Glu,His, Lys, Met Lys, Met, Ser Glu Asp Asp, Gln, Lys Arg, Asn, Asp, Gln,His, Lys, Ser Gly Pro Ala Ala, Ser His Asn; Gln Arg, Asn, Gln, Tyr Arg,Asn, Gln, Glu, Tyr Ile Leu; Val Leu, Met, Val Leu, Met, Phe, Val LeuIle; Val Ile, Met, Phe, Val Ile, Met, Phe, Val Lys Arg; Gln; Glu Arg,Asn, Gln, Glu Arg, Asn, Gln, Glu, Ser, Met Leu; Ile Gln, Ile, Leu, ValGln, Ile, Leu, Phe, Val Phe Met; Leu; Tyr Leu, Trp, Tyr Ile, Leu, Met,Trp, Tyr Ser Thr Ala, Asn, Thr Ala, Asn, Asp, Gln, Glu, Gly, Lys, ThrThr Ser Ala, Asn, Ser Ala, Asn, Ser, Val Trp Tyr Phe, Tyr Phe, Tyr TyrTrp; Phe His, Phe, Trp His, Phe, Trp Val Ile; Leu Ile, Leu, Met Ala,Ile, Leu, Met, Thr

In some embodiments, an ER, HER2, Ki-67 or PR sequence is a sequencevariant of a native ER, HER2, Ki-67 or PR sequence, respectively, suchas a nucleic acid or protein sequence that has at least 99%, at least98%, at least 95%, at least 92%, at least 90%, at least 85%, at least80%, at least 75%, at least 70%, at least 65%, or at least 60% sequenceidentity to the sequences set forth in a GenBank® accession numberreferred to herein, wherein the resulting variant retains ER, HER2,Ki-67 or PR biological activity. “Sequence identity” is a phrasecommonly used to describe the similarity between two amino acidsequences (or between two nucleic acid sequences). Sequence identitytypically is expressed in terms of percentage identity; the higher thepercentage, the more similar the two sequences.

Methods for aligning sequences for comparison and determining sequenceidentity are well known in the art. Various programs and alignmentalgorithms are described in: Smith and Waterman, Adv. Appl. Math.,2:482, 1981; Needleman and Wunsch, J. Mol. Biol., 48:443, 1970; Pearsonand Lipman, Proc. Natl. Acad. Sci. USA, 85:2444, 1988; Higgins andSharp, Gene, 73:237-244, 1988; Higgins and Sharp, CABIOS, 5:151-153,1989; Corpet et al., Nucleic Acids Research, 16:10881-10890, 1988;Huang, et al., Computer Applications in the Biosciences, 8:155-165,1992; Pearson et al., Methods in Molecular Biology, 24:307-331, 1994;Tatiana et al., FEMS Microbiol. Lett., 174:247-250, 1999. Altschul etal. present a detailed consideration of sequence-alignment methods andhomology calculations (J. Mol. Biol., 215:403-410, 1990).

The National Center for Biotechnology Information (NCBI) Basic LocalAlignment Search Tool (BLAST™, Altschul et al., J. Mol. Biol.,215:403-410, 1990) is publicly available from several sources, includingthe National Center for Biotechnology Information (NCBI, Bethesda, Md.)and on the Internet, for use in connection with the sequence-analysisprograms blastp, blastn, blastx, tblastn and tblastx. A description ofhow to determine sequence identity using this program is available onthe Internet under the help section for BLAST™.

For comparisons of amino acid sequences of greater than about 15 aminoacids, the “Blast 2 sequences” function of the BLAST™ (Blastp) programis employed using the default BLOSUM62 matrix set to default parameters(cost to open a gap [default=5]; cost to extend a gap [default=2];penalty for a mismatch [default=3]; reward for a match [default=1];expectation value (E) [default=10.0]; word size [default=3]; and numberof one-line descriptions (V) [default=100]. When aligning short peptides(fewer than around 15 amino acids), the alignment should be performedusing the Blast 2 sequences function “Search for short nearly exactmatches” employing the PAM30 matrix set to default parameters (expectthreshold=20000, word size=2, gap costs: existence=9 and extension=1)using composition-based statistics.

C. Detection of Proteins

In particular examples, a sample obtained from the subject is analyzedto determine if it contains detectable levels of ER, HER2, Ki-67 and PRprotein, such as a breast cancer sample. Thus, the sample can beanalyzed to detect or measure the presence of ER, HER2, Ki-67 and PRproteins in the sample, for example a qualitative or quantitativemeasurement. The expression patterns of ER, HER2, Ki-67 and PR proteinscan also be used to determine the heterogeneity of the proteinexpression.

Methods of detecting proteins are routine. In some examples,immunoassays are used to detect the presence of ER, HER2, Ki-67 and PRproteins in the sample. Generally, immunoassays include the use of oneor more specific binding agents (such as antibodies) that cansubstantially only bind to the target peptide, such as ER, HER2, Ki-67and PR. Such binding agents can include a detectable label (such as aradiolabel, fluorophore or enzyme), that permits detection of thebinding to the protein. Exemplary immunoassays that can be used include,but are not limited to: Western blotting, ELISA, fluorescencemicroscopy, and flow cytometry. A particular immunoassay isimmunohistochemistry.

In one example, the specific binding agent is an antibody, such as apolyclonal or monoclonal antibody, or fragment thereof. In someexamples, the antibody is a humanized antibody. In some examples, theantibody is a chimeric antibody. If desired, the antibody can include adetectable label to permit detection and in some cases quantification ofthe target protein/antibody complex. In other examples, the antibody isdetected with an appropriate labeled secondary antibody.

In some examples, the antibodies for ER, PR, HER2 and Ki-67 are obtainedfrom Ventana Medical Systems, Inc. (Tucson, Ariz.). However, one skilledin the art will appreciate that other antibodies that can be used in themethods and kits provided herein are commercially available from othersources, such as: Novus Biologicals (Littleton, Colo.), Santa Cruzbiotechnology, Inc. (Santa Cruz, Calif.), Abcam (Cambridge, Mass.), andInvitrogen (Carlsbad, Calif.).

The presence of detectable signal above background or control levelsindicates the presence of a target peptide (e.g., ER, HER2, Ki-67 and PRprotein) in the sample. The value obtained for the test breast cancersample can be compared to a reference value, such as a reference valuerepresenting a value or range of values expected. In some examples, thereference is a sample possessing a known or expected amount of ER, HER2,Ki-67 and PR protein.

In some examples, a breast cancer sample is obtained, and processed forIHC. For example, the sample can be fixed and embedded, for example withformalin and paraffin. The sample can then be mounted on a support, suchas a glass microscope slide. For example, the sample can be microtomedinto a series of thin sections, and the sections mounted onto one ormore microscope slides. In some examples, a single slide includesmultiple tissue sections. Different sections of the breast cancer samplecan then be individually labeled with antibodies specific for ER, HER2,Ki-67 or PR protein. That is, one section can be labeled withER-specific antibodies, and another section can be labeled withHER2-specific antibodies, and so on. In some examples, a single sectionof the breast cancer sample can be labeled with antibodies specific fortwo or more of ER, HER2, Ki-67 or PR protein. That is, one section canbe labeled with ER-specific antibodies and with HER2-specificantibodies, and the antibodies distinguish by using different labels(wherein each label is specific for ER, HER2, Ki-67 or PR). For example,the BenchMark ULTRA from Ventana Medical Systems, Inc. can be used tostain and process the slides.

In some examples the slides containing the labeled sample are scannedand digitized, for example using the iScan Coreo (Ventana). Thus, foreach of ER, HER2, Ki-67 and PR protein (which are detectably labeled inthe sample), at least one digital image can be obtained. Subsequently,two or more fields of view (FOV) for each protein (ER, HER2, Ki-67 andPR protein) are selected and annotated, for example by a pathologist.

D. Calculating an IHC Combination Score

To calculate the IHC combination (e.g., IHC4) score, a breast cancersample with detectably labeled ER, HER2, Ki-67 and PR (for example oneor more slides, such as 1, 2, 3 or 4 slides) is used. In one example,the breast cancer sample can be labeled with primary antibodies specificfor each of ER, HER2, Ki-67 and PR, and appropriately labeled secondaryantibodies (such as those labeled with a fluorophore or enzyme, such asDIG). In one example, the ultraView Red ISH DIG detection Kit is used asper the manufacturer's instructions (Ventana Medical Systems, Inc.,Catalog #760-505).

One or more digital images of one or more slides containing the labeledbreast cancer sample can be obtained, for example using microscope imagescanning software. The one or more slides containing the one or morelabeled breast cancer samples (or a digital image thereof) is evaluatedfor its overall staining, for example by a pathologist. For example,regions of heterogeneity in the staining can be detected for each of ER,HER2, Ki-67 and PR. If regional heterogeneity is observed, at least oneFOV having regional heterogeneity is selected (such as at least 2, atleast 3, at least 4, or at least 5 different FOV areas of regionalheterogeneity on the slide, such as 2, 3, 4, 5, 6, 7, 8, 9, or 10 FOVs),along with at least two other FOV that are similar to one another (suchas at least 3, at least 4, or at least 5 different FOV areas on theslide that are similar to one another, such as 2, 3, 4, 5, 6, 7, 8, 9,or 10 FOVs). If no regional heterogeneity is observed, fewer FOV can beselected, such as at least 2, at least 3, at least 4, or at least 5different FOV on the slide, such as 2, 3, 4 or 5 FOV on the slide.

For each FOV selected for PR and Ki-67 protein, the percent positivityis determined. Thus, if four FOV are selected for PR, then % positivityis determined for each of the four FOV. To calculate the % positivity,the total number of tumor cells in the FOV as well as the total numberof tumor cells stained in the FOV (e.g., that are PR+) is determined.The % positivity is the total number of tumor cells stained in the FOVdivided by the total number of tumor cells in the FOV. The % positivityfor each FOV for a particular marker (e.g., PR) is averaged, resultingin a % positivity for PR and Ki-67.

For each FOV for HER2, a binned score (on a sale of 0-3) is determined.For example, for each FOV, a binned score between 0 and 3 can beassigned to the staining based on evaluation of the FOV. A four-pointscale can be used to describe the immunostaining of membrane surroundinga nucleus for HER2, as follows: 0, negative; 1, weak positivity; 2,moderate positivity; and 3+, strong positivity, for example as shown inTable 2. Cytoplasmic staining may still be present, but such stainingneed not be included in the determination.

TABLE 2 HER2 binned scores HER2 Staining Staining Pattern ScoreAssessment No membrane staining is observed 0  Negative Faint, partialstaining of the membrane 1+ Negative Weak complete staining of themembrane, 2+ Positive greater than 10% of cancer cells Intense completestaining of the membrane, 3+ Positive greater than 10% of cancer cellsAlternatively, a binary score (0 or 1) can be determined for HER2 (e.g.,based on individual FOVs or the FOVs collectively), wherein a positiveresult (1) corresponds to a staining score of 2 or 3, and a negativeresult (0) corresponds to a staining score of 0 or 1. Greater or lessresolution can be used. For example a system using 0, 1, 2, and 3 can beused. Internally, the score for HER2 can be represented as a binnedscore (e.g., 0, 1, 2, or 3) or as a binary value (e.g., 0 or 1) (e.g.,the binary value is determined based on the binned score).

For the FOVs selected for ER, an H-score is determined (e.g., for theindividual FOVs or the FOVs collectively). The H-score for ER is thepercentage of cells showing weak staining, added to two times thepercentage of cells staining moderately, added to three times thepercentage of cells staining intensely, that is divided by 30 to arriveat a variable between 0-10 (ER₁₀). An H-score of more than 1 ispositive. The percentage of cells staining positive for PR (e.g., cappedat 10%) was divided by 10 to obtain a variable between 0 and 10 (PR₁₀).

Thus, after FOVs are identified by a pathologist for each of the fourmarkers, an imaging algorithm can extract digital information from theslide (such as a breast cancer sample labeled to detect ER, HER2, Ki-67or PR protein), transforming the digital image into score components(e.g., measurements of % positivity, binned (or binary) score, andH-score). These scores for each marker described above are used togenerate an IHC combination score, which is calculated using theformula:IHC4=94.7×{−0.100ER ₁₀−0.079PR ₁₀+0.586HER2+0.240 ln(1+10×Ki67)}

E. Calculating a Heterogeneity Score

Heterogeneity refers to the spatial variation of histochemical andmolecular staining patterns, such as the staining patterns for breastcancer biomarkers ER, HER2, Ki-67 and PR in a breast cancer sample.Heterogeneity can be an indicator of the spatial variation of tumoraggressiveness and/or growth patterns that can be correlated with anaggregated clinical phenotype (e.g., a tumor likely to recur). It isshown herein that biological heterogeneity of ER, PR, HER2 and Ki-67protein expression is correlated with the unpredictable recurrence of afraction of early stage breast cancer patients.

The method includes quantifying the regional heterogeneity in the samplefor each of the four markers, which is computed from the scores from theselected FOV. Thus, for each marker, a quantitative measure of regionalheterogeneity is determined. If the marker is homogenous or notdetectable (e.g., HER2−), then that value will fall out of theheterogeneity score calculation. Two or more fields of view (FOV) foreach protein (ER, HER2, Ki-67 and PR protein) are selected andannotated, for example by a pathologist. The FOV selected can be thesame or different from the FOV selected to calculate the IHC4 score.

In some examples, a plurality of FOVs (e.g., at least 3, at least 4, orat least 5 FOVs, or the like) are selected, for example, 2, 3, 4, 5, 6,7, 8, 9 or 10 FOVs. As described above, responsive to observing regionalheterogeneity, more FOVs may be selected, such as at least one FOVhaving regional heterogeneity (such as at least 2, at least 3, at least4, or at least 5 different FOV areas of regional heterogeneity on theslide, such as 2, 3, 4, 5, 6, 7, 8, 9, or 10 FOVs), along with at leasttwo other FOVs that are similar to one another (such as at least 3, atleast 4, or at least 5 different FOV areas on the slide that are similarto one another, such as 2, 3, 4, 5, 6, 7, 8, 9, or 10 FOVs). Responsiveto observing no regional heterogeneity, fewer FOVs may be selected, suchas at least 2, at least 3, at least 4, or at least 5 different FOVs onthe slide, such as 2, 3, 4 or 5 FOVs on the slide.

In some cases, it may be desirable to select additional FOVs toconclusively establish (e.g., confirm or prove) that regionalheterogeneity is not present. However, if it is conclusively apparentthat there is no regional heterogeneity, selecting additional FOVs mayunnecessarily expend resources; therefore, fewer FOVs may be selected.

In one example, FOVs with different % positivity are selected, as theseareas indicate heterogeneity, for example regional heterogeneity.

For each of ER, Ki-67, HER2 and PR protein, a heterogeneity score isdetermined. In some examples, HER2 is not determined (or the value fallsout as there is no heterogeneity for HER2), for example if the sample isHER2 negative. In some examples, Ki-67 is not determined (or the valuefalls out as there is no heterogeneity for Ki-67).

For the ER and PR markers (and in some examples also HER2 and Ki-67) theheterogeneity score is calculated as follows. The variability of percentpositivity scores are calculated from heterogeneous regions in theselected FOV. For example, a variability metric (VM) for each marker canbe determined, wherein the VM is an indication of how different oneselected FOV is from another FOV for the same protein. There are manyways this can be calculated, such as determining a standard deviation.In one example, the VM for each of ER, PR, and Ki-67 (and in someexamples HER2) is calculated as follows:VM=STD(PPFS ₁),PP(FS ₂), . . . PP(FS _(N)))wherein PP(FS) is the percent positivity for each field of view, FS.Percent positivity can be calculated as described herein. Then theheterogeneity score for the marker (e.g., ER and PR) is calculated usingthe formula (where α=[0,1]:

$H = \left\{ {\begin{matrix}{{\alpha*\frac{V\; M}{0.05}},{S < {10\%}}} \\{{\alpha*\frac{V\; M}{S}},{otherwise}}\end{matrix},} \right.$

For HER2, a binned score, P(FS) (0 to 3 scale, see above) is computedfor each FOV, FS. HER2 heterogeneity score can then calculated using theformula:

$H = {\sum\limits_{\underset{i \neq j}{{\forall i},j}}{{{P\left( {FS}_{i} \right)} - {P\left( {FS}_{j} \right)}}}}$If the binned scores are the same for each FOV, the heterogeneity scorefor HER2 is 0.

The following table indicates different types of heterogeneity,including regional heterogeneity as described herein.

TABLE 3 Types of heterogeneity ER PR Ki67 HER2 HER2 Generic proteinprotein protein protein gene Geographic Diffuse - focal Not n.a. n.a.n.a. n.a 2 separate blocks, applicable >2 in apart Regional Diffuse -focal Yes Yes Yes Yes Yes 1 4x-FOV apart, same section/ block, 0.25-2 inInter-glandular > or <50% No No No Yes Yes Within 4x-FOV, similarformations <0.25 in For both regional categories Intra-glandular = > or<50% Yes Yes Yes Yes Yes cell-cell similar cells All formations in Ifmultilayered, all No No No No No 1 20x-FOV or specific layers For allregional Diffuse - polarized No No No No No and inter-glandularcategories Sub-cellular Nuclear, Nuclear Nuclear Nuclear MembranousNuclear cytoplasmic or membraeous For all regional, inter- and intra-glandular categories Intensity Negative, 3 positive Yes Yes Yes Yes Yescategories

F. Calculating an Output Prognosis Score

The output prognosis score is calculated from the IHC combination scoreand the heterogeneity score described above. A combined heterogeneityscore is generated as follows and can be adjusted to include furtherbiomarkers:Combined heterogeneity score=square root(ER heterogeneity score+PRheterogeneity score)

The IHC combination score and the combined heterogeneity score areentered into a statistical analysis method (e.g., Cox proportionalhazards model), to maximize the combined predictive capabilities of bothmeasures. For example the model can be used to determine theprogression-free survival (PFS) as outcome, such as a 1-, 3- or 5-yearPFS or otherwise separate patients into categories.

The result is a mathematical formula that generates a score to predictthe risk of 5-year PFSoutput prognosis score=0.03114*IHC4+1.95119*combined heterogeneity score

Thus, the output prognosis score either is a high risk score (whichindicates that early stage breast cancer is likely to recur, for examplewithin 5 years, such as a local recurrence or a distant metastasis) or alow risk score (which indicates that early stage breast cancer is notlikely to recur).

G. Outputting Output Value and Prognosis

Following the determination of IHC combination score, heterogeneityscore, and the output prognosis score, the assay results (such as theoutput prognosis score), findings, prognosis, predictions and/ortreatment recommendations are typically recorded and communicated totechnicians, physicians and/or patients, for example. In certainembodiments, computers will be used to communicate such information tointerested parties, such as, patients and/or the attending physicians.Based on the prognosis of the breast cancer, the therapy administered toa subject can be modified.

In one embodiment, a prognosis, prediction and/or treatmentrecommendation based on the output value is communicated to interestedparties as soon as possible after the assay is completed and theprognosis is generated. The results and/or related information may becommunicated to the subject by the subject's treating physician.Alternatively, the results may be communicated directly to interestedparties by any means of communication, including writing, such as byproviding a written report, electronic forms of communication, such asemail, or telephone. Communication may be facilitated by use of asuitably programmed computer, such as in case of email communications.In certain embodiments, the communication containing results of aprognostic test and/or conclusions drawn from and/or treatmentrecommendations based on the test, may be generated and deliveredautomatically to interested parties using a combination of computerhardware and software which will be familiar to artisans skilled intelecommunications. One example of a healthcare-oriented communicationssystem is described in U.S. Pat. No. 6,283,761; however, the presentdisclosure is not limited to methods which utilize this particularcommunications system.

In certain embodiments of the methods of the disclosure, all or some ofthe method steps, including the assaying of samples, prognosis of breastcancer, and communicating of assay results or prognosis, may be carriedout in diverse (e.g., foreign) jurisdictions.

H. Follow-Up Therapies

The disclosed methods can further include selecting subjects fortreatment for breast cancer, for example if the sample is prognosed asan early stage breast cancer likely to recur. Alternatively, thedisclosed methods can further include selecting subjects for notreatment (for example just monitoring), if the sample is diagnosed asan early stage breast cancer not likely to recur.

In some embodiments, the disclosed methods include one or more of thefollowing depending on the patient's prognosis: a) prescribing atreatment regimen for the subject if the subject's determined prognosisis that the early stage breast cancer is likely to recur (such astreatment with one or more radiotherapies and/or chemotherapeuticagents, additional surgery, more frequent monitoring, or combinationsthereof); b) not prescribing a treatment regimen for the subject if thesubject's determined prognosis is that the early stage breast cancer isnot likely to recur; c) administering a treatment (such as treatmentwith one or more radiotherapies and/or chemotherapeutic agents,additional surgery, or combinations thereof) to the subject if thesubject's determined prognosis is that the early stage breast cancer islikely to recur; and d) not administering a treatment regimen to thesubject if the subject's determined prognosis is that the early stagebreast cancer is not likely to recur. In an alternative embodiment, themethod can include recommending one or more of (a)-(d). Thus, thedisclosed methods can further include treating a subject for breastcancer.

Exemplary Score Components

In any of the examples herein, score components can refer to measurementof gene expression (e.g., percent positivity or the like), H-score,binned score, binary score, or the like for one or more biomarkers.

As components are combined, the resulting components can representplural biomarkers.

Exemplary Further Description

For IHC computation and heterogeneity measures, in place of manualmicroscopy scores, the slides can be digitized and automated imageanalysis algorithms can compute the percent positivity, binned score(e.g., and convert to binary score) and H-Score for the fields (FOVs)selected by the pathologist.

In each digitized slide associated with the above-mentioned fourmarkers, the pathologist annotates specific regions to quantify theinter-region intensity and regional heterogeneity. Using the automatedimage analysis for each region, positive and negative stained cellcounts, percent positivity, and binned score (0, 1+, 2+, 3+) arecomputed.

To quantify the inter-region heterogeneity, heterogeneity metrics basedon these regional scores can be used. The heterogeneity score is basedon the negative and positive (1+, 2+ and 3+) cell counts in the FOVsselected by the pathologist. The heterogeneity score captures thevariation of scores within the selected FOVs. Using the FOVs annotatedby the pathologist, the score is a normalized function of the standarddeviation of the scores from different FOVs. The chosen normalizationparameter is marker-dependent, reflecting the fact that in generalinter-region percent positivity score have higher variability for higheroverall slide positivity score.

Exemplary Computing System

FIG. 23 illustrates a generalized example of a suitable computing system2300 in which several of the described innovations may be implemented.The computing system 2300 is not intended to suggest any limitation asto scope of use or functionality, as the innovations may be implementedin diverse general-purpose or special-purpose computing systems.Computing systems as described herein can be used to implement automatedfunctionality (e.g., processes, actions, and the like are performed by acomputing system as described herein).

With reference to FIG. 23, the computing system 2300 includes one ormore processing units 2310, 2315 and memory 2320, 2325. In FIG. 23, thisbasic configuration 2330 is included within a dashed line. Theprocessing units 2310, 2315 execute computer-executable instructions. Aprocessing unit can be a general-purpose central processing unit (CPU),processor in an application-specific integrated circuit (ASIC) or anyother type of processor. In a multi-processing system, multipleprocessing units execute computer-executable instructions to increaseprocessing power. For example, FIG. 23 shows a central processing unit2310 as well as a graphics processing unit or co-processing unit 2315.The tangible memory 2320, 2325 may be volatile memory (e.g., registers,cache, RAM), non-volatile memory (e.g., ROM, EEPROM, flash memory,etc.), or some combination of the two, accessible by the processingunit(s). The memory 2320, 2325 stores software 2380 implementing one ormore innovations described herein, in the form of computer-executableinstructions suitable for execution by the processing unit(s).

A computing system may have additional features. For example, thecomputing system 2300 includes storage 2340, one or more input devices2350, one or more output devices 2360, and one or more communicationconnections 2370. An interconnection mechanism (not shown) such as abus, controller, or network interconnects the components of thecomputing system 2300. Typically, operating system software (not shown)provides an operating environment for other software executing in thecomputing system 2300, and coordinates activities of the components ofthe computing system 2300.

The tangible storage 2340 may be removable or non-removable, andincludes magnetic disks, magnetic tapes or cassettes, CD-ROMs, DVDs, orany other medium which can be used to store information in anon-transitory way and which can be accessed within the computing system2300. The storage 2340 stores instructions for the software 2380implementing one or more innovations described herein.

The input device(s) 2350 may be a touch input device such as a keyboard,mouse, pen, or trackball, a voice input device, a scanning device, oranother device that provides input to the computing system 2300. Forvideo encoding, the input device(s) 2350 may be a camera, video card, TVtuner card, or similar device that accepts video input in analog ordigital form, or a CD-ROM or CD-RW that reads video samples into thecomputing system 2300. The output device(s) 2360 may be a display,printer, speaker, CD-writer, or another device that provides output fromthe computing system 2300.

The communication connection(s) 2370 enable communication over acommunication medium to another computing entity. The communicationmedium conveys information such as computer-executable instructions,audio or video input or output, or other data in a modulated datasignal. A modulated data signal is a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal. By way of example, and not limitation, communicationmedia can use an electrical, optical, RF, or other carrier.

The innovations can be described in the general context ofcomputer-readable media. Computer-readable media are any availabletangible media that can be accessed within a computing environment. Byway of example, and not limitation, with the computing system 2300,computer-readable media include memory 2320, 2325, storage 2340, andcombinations of any of the above.

The innovations can be described in the general context ofcomputer-executable instructions, such as those included in programmodules, being executed in a computing system on a target real orvirtual processor. Generally, program modules include routines,programs, libraries, objects, classes, components, data structures, etc.that perform particular tasks or implement particular abstract datatypes. The functionality of the program modules may be combined or splitbetween program modules as desired in various embodiments.Computer-executable instructions for program modules may be executedwithin a local or distributed computing system.

The terms “system” and “device” are used interchangeably herein. Unlessthe context clearly indicates otherwise, neither term implies anylimitation on a type of computing system or computing device. Ingeneral, a computing system or computing device can be local ordistributed, and can include any combination of special-purpose hardwareand/or general-purpose hardware with software implementing thefunctionality described herein.

For the sake of presentation, the detailed description uses terms like“determine” and “use” to describe computer operations in a computingsystem. These terms are high-level abstractions for operations performedby a computer, and should not be confused with acts performed by a humanbeing. The actual computer operations corresponding to these terms varydepending on implementation.

Computer-Readable Media

Any of the computer-readable media herein can be non-transitory (e.g.,memory, magnetic storage, optical storage, or the like).

Any of the storing actions described herein can be implemented bystoring in one or more computer-readable media (e.g., computer-readablestorage media or other tangible media).

Any of the things described as stored can be stored in one or morecomputer-readable media (e.g., computer-readable storage media or othertangible media).

Any of the methods described herein can be implemented bycomputer-executable instructions in (e.g., encoded on) one or morecomputer-readable media (e.g., computer-readable storage media or othertangible media). Such instructions can cause a computer to perform themethod. The technologies described herein can be implemented in avariety of programming languages.

Methods in Computer-Readable Storage Devices

Any of the methods described herein can be implemented bycomputer-executable instructions stored in one or more computer-readablestorage devices (e.g., memory, magnetic storage, optical storage, or thelike). Such instructions can cause a computer to perform the method.

Exemplary Early Stage Breast Cancer Recurrence Risk Implementation andResults

This example describes methods used to predict early stage breast cancerrecurrence risk.

Breast cancer samples from about 20 patients were labeled withantibodies specific for ER, PR, HER2, and Ki-67.

These resulting labeled samples on microscope slides were imaged usingdigital pathology. Three FOV were selected for each of ER, PR, HER2, andKi-67. Software was used to quantify percent cell staining (e.g.,percent positivity) and intensity for each FOV. Percent positivity wasthus determined. In addition, three different measures of heterogeneitybased on the variability of intensities and percent cells staining forthe three FOV's were determined.

The resulting percent cell staining and intensity for each FOV for eachof ER, PR, HER2, and Ki-67 were averaged (e.g., the ER FOVs wereaveraged). This results in a percent cell staining value and intensityvalue for each of ER, PR, HER2, and Ki-67. These values were used tocalculate the IHC4 score using this formula:IHC4=94.7×{−0.100ER10−0.079PR10+0.586HER2+0.240 ln(1+10×Ki67)}

Using the IHC4 score and the 12 heterogeneity values (one for each ofthe three FOV for each of ER, PR, HER2, and Ki-67), each of theheterogeneity measures and the IHC4 score were modeled using the coxproportional hazards model, which models time to distant recurrence. Thethree heterogeneity measures were very similar, thus the firstheterogeneity measure for each of the four assays was used.

Using the five variables, the IHC4 score, and one heterogeneity scorefor each assay (ER, PR, Ki-67 and HER2), the IHC4 score was modeled witheach of the heterogeneity scores separately (using the cox model).

Based on the results, it was determined that the PR heterogeneity scoremeasure was the most useful for determining time to distant recurrence,with ER adding some predictive power.

To determine if the relationship of heterogeneity score and risk waslinear or not; as there is an assumption that each unit change inheterogeneity score results in the same increase in risk of distantrecurrence. The data was transformed (taking square root, natural log,etc.) and the transformed variable entered into the cox model. Thesquare root was selected as it increased the predictive ability themost.

The final modification of the measurement of heterogeneity involvedtaking the heterogeneity score (HET) for ER and PR, summing them andtaking the square root. Heterogeneity score=squareroot(HET^(PR)+HET^(ER)). The resulting HET score and the IHC4 score wereentered into a cox proportional hazards model which takes the two linearvariables and finds the best linear combination of the two to predicttime to distant recurrence. The resulting formula is:Output risk score=1.95119*(square root(HET^(PR)+HET^(ER)))+0.03114*IHC4

As shown in FIG. 24, this method accurately classified a breast cancersample as one that is more (progression) or less (no progression)aggressive. The cases shown in FIG. 24 are the same cases used to createthe algorithm.

Alternatives

The technologies from any example can be combined with the technologiesdescribed in any one or more of the other examples. In view of the manypossible embodiments to which the principles of the disclosure may beapplied, it should be recognized that the illustrated embodiments areonly examples of the disclosure and should not be taken as limiting thescope of the disclosure. Rather, the scope of the disclosure is definedby the following claims. We therefore claim as our invention all thatcomes within the scope and spirit of these claims.

The invention claimed is:
 1. A method of predicting breast cancerrecurrence in a human subject diagnosed with breast cancer, comprising:(a) obtaining one or more tissue samples from the human subjectdiagnosed with breast cancer, wherein the obtained one or more tissuesamples are stained for the presence of at least two biomarkers forcomputing an immunohistochemistry combination score and stained for thepresence of at least two biomarkers for computing an heterogeneitycombination score; (b) computing the immunohistochemistry (IHC)combination score, wherein the computing of the IHC combination scorecomprises: receiving a plurality of fields of view (FOV) for computingthe IHC combination score, wherein the plurality of received IHCcombination score FOVs are derived from images of the one or more tissuesamples stained for the presence of the at least two biomarkers forcomputing the IHC combination score, and wherein the plurality ofreceived IHC combination score FOVs include at least two FOVs for eachof the at least two biomarkers for computing the IHC combination score;measuring protein expression levels for each of the at least twobiomarkers for computing the IHC combination score within each of theplurality of received IHC combination score FOVs using an automatedimage analysis algorithm; and computing the IHC combination score basedon the measured protein expression levels for each of the at least twobiomarkers for computing the IHC combination score; (c) computing theheterogeneity combination score, wherein the computing of theheterogeneity combination score comprises: receiving a plurality of FOVsfor computing the heterogeneity combination score, wherein the pluralityof received heterogeneity combination score FOVs are derived from imagesof the one or more tissue samples stained for the presence of the atleast two biomarkers for computing the heterogeneity combination score,and wherein the plurality of received heterogeneity combination scoreFOVs include at least two FOVs for each of the at least two biomarkersfor computing the heterogeneity combination score; measuring proteinexpression levels for each of the at least two biomarkers for computingthe heterogeneity combination score within each of the plurality ofreceived heterogeneity combination score FOVs using an automated imageanalysis algorithm; independently deriving a heterogeneity score foreach of the at least two biomarkers for computing the heterogeneitycombination score based on the measured protein expression levels foreach of the at least two biomarkers for computing the heterogeneitycombination score; and computing the heterogeneity combination scorebased on the independently derived heterogeneity scores for each of theat least two biomarkers for computing the heterogeneity combinationscore; (d) computing a breast cancer recurrence prognosis score bycombining and weighting the computed immunohistochemistry combinationscore and the computed heterogeneity combination score; (e) determiningwhether the human subject diagnosed with breast cancer is at a high riskor at a low risk of breast cancer recurrence based on the computedbreast cancer recurrence prognosis score; and f) administering to thehuman patient an appropriate breast cancer treatment regimen if thebreast cancer recurrence prognosis score is greater than thepredetermined threshold value.
 2. The method of claim 1, wherein theleast two biomarkers for computing the IHC combination score areselected from the group consisting of an estrogen receptor (ER), a humanepidermal growth factor receptor 2 (HER2), a Ki-67 biomarker, and aprogesterone receptor (PR).
 3. The method of claim 1, wherein themeasuring of the protein expression values for each of the at least twobiomarkers for computing the IHC combination score comprises calculatingat least one of a percent positivity or an H-score.
 4. The method ofclaim 1, wherein the at least two biomarkers for computing theheterogeneity combination score are selected from the group consistingof an estrogen receptor (ER), a human epidermal growth factor receptor 2(HER2), a Ki-67 biomarker, and a progesterone receptor (PR).
 5. Themethod of claim 4, wherein the receiving of the plurality of FOVs forthe computing of the heterogeneity combination score comprises receivingat least two FOVs for each of ER, HER2, Ki-67, and PR.
 6. The method ofclaim 1, further comprising providing a treatment recommendation forbreast cancer based on the computed breast cancer recurrence prognosisscore.
 7. The method of claim 1, wherein the determination of the breastcancer recurrence prognosis is determined by comparing the computedbreast cancer recurrence prognosis score to a predetermined thresholdvalue.
 8. The method of claim 2, wherein the IHC combinations score isan IHC4 score.
 9. The method of claim 1, wherein each of theindependently derived heterogeneity scores are determined by assessing avariability of the measured protein expression levels for one of the atleast two biomarkers for computing the heterogeneity combination scorebetween at least two of the received plurality of heterogeneitycombination score FOVs.
 10. The method of claim 1, wherein each of theindependently derived heterogeneity scores are normalized prior tocomputing the heterogeneity combination score.
 11. A method of treatinga human subject diagnosed with breast cancer, comprising: (a) obtainingone or more tissue samples from the human subject diagnosed with breastcancer, wherein the obtained one or more tissue samples are stained forthe presence of at least two biomarkers for computing animmunohistochemistry combination score and stained for the presence ofat least two biomarkers for computing an heterogeneity combinationscore; (b) computing the immunohistochemistry (IHC) combination score,wherein the computing of the IHC combination score comprises: receivinga plurality of fields of view (FOV) for computing the IHC combinationscore, wherein the plurality of received IHC combination score FOVs arederived from images of of the one or more tissue samples stained for thepresence of at least two biomarkers for computing the IHC combinationscore, and wherein the plurality of received IHC combination score FOVsinclude at least two FOVs for each of the at least two biomarkers forcomputing the IHC combination score; measuring protein expression levelsfor each of the at least two biomarkers for computing the IHCcombination score within each of the plurality of received IHCcombination score FOVs using an automated image analysis algorithm; andcomputing the IHC combination score based on the measured proteinexpression levels for each of the at least two biomarkers for computingthe IHC combination score; (c) computing a heterogeneity combinationscore, wherein the computing of the heterogeneity combination scorecomprises: receiving a plurality of FOVs for computing the heterogeneitycombination score, wherein the plurality of received heterogeneitycombination score FOVs are derived from images of the one or more tissuesamples stained for the presence of at least two biomarkers forcomputing the heterogeneity combination score, and wherein the pluralityof received heterogeneity combination score FOVs include at least twoFOVs for each of the at least two biomarkers for computing theheterogeneity combination score; measuring protein expression levels foreach of the at least two biomarkers for computing the heterogeneitycombination score within each of the plurality of received heterogeneitycombination score FOVs using an automated image analysis algorithm;independently deriving a heterogeneity score for each of the at leasttwo biomarkers for computing the heterogeneity combination score basedon the measured protein expression levels for each of the at least twobiomarkers for computing the heterogeneity combination score; andcomputing the heterogeneity combination score based on the independentlyderived heterogeneity scores for each of the at least two biomarkers forcomputing the heterogeneity combination score; (d) computing a breastcancer recurrence prognosis score by combining and weighting thecomputed immunohistochemistry combination score and the computedheterogeneity combination score; (e) determining abreast cancerrecurrence prognosis based on the computed breast cancer recurrenceprognosis score for the subject previously treated for breast cancer;and (f) identifying an appropriate therapy for the human subjectdiagnosed with breast cancer based on the provided determination ofbreast cancer recurrence; and g) administering to the human patient anappropriate breast cancer treatment regimen if the breast cancerrecurrence prognosis score is greater than the predetermined thresholdvalue.